{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "77gENRVX40S7"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "d8jyt37T42Vf"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "aPxHdjwW5P2j"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet                                                                                                                    # IGNORE_COPYRIGHT: cleared by OSS licensing\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hRTa3Ee15WsJ"
   },
   "source": [
    "# Transfer Learning Using Pretrained ConvNets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dQHMcypT3vDT"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/alpha/tutorials/images/transfer_learning\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/images/transfer_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/images/transfer_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2X4KyhORdSeO"
   },
   "source": [
    "In this tutorial you will learn how to classify cats vs dogs images by using transfer learning from a pre-trained network.\n",
    "\n",
    "\n",
    "A **pre-trained model** is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as it is, or use **transfer learning** to customize this model to a given task.\n",
    "\n",
    "The intuition behind transfer learning is that if a model trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. You can then take advantage of these learned feature maps without having to start from scratch training a large model on a large dataset.\n",
    "\n",
    "In this notebook, you will try two ways to customize a pretrained model:\n",
    "\n",
    "1. **Feature Extraction**: Use the representations learned by a previous network to extract meaningful features from new samples. You simply add a new classifier, which will be trained from scratch, on top of the pretrained model so that you can repurpose the feature maps learned previously for our dataset.\n",
    "\n",
    "  You do not need to (re)train the entire model.  The base convolutional network already contains features that are generically useful for classifying pictures. However, the final, classification part of the pretrained model is specific to original classification task, and subsequently specific to the set of classes on which the model was trained.\n",
    "\n",
    "2. **Fine-Tuning**: Unfreezing a few of the top layers of a frozen model base and jointly training both the newly-added classifier layers and the last layers of the base model. This allows us to \"fine tune\" the higher-order feature representations in the base model in order to make them more relevant for the specific task.\n",
    "\n",
    "You will follow the general machine learning workflow.\n",
    "\n",
    "1. Examine and understand the data\n",
    "2. Build an input pipeline, in this case using Keras `ImageDataGenerator`\n",
    "3. Compose our model\n",
    "  * Load in our pretrained base model (and pretrained weights)\n",
    "  * Stack our classification layers on top\n",
    "4. Train our model\n",
    "5. Evaluate model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iBMcobPHdD8O"
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TqOt6Sv7AsMi"
   },
   "outputs": [],
   "source": [
    "!pip install tensorflow-gpu==2.0.0-alpha0\n",
    "import tensorflow as tf\n",
    "\n",
    "keras = tf.keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v77rlkCKW0IJ"
   },
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0GoKGm1duzgk"
   },
   "source": [
    "### Data download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vHP9qMJxt2oz"
   },
   "source": [
    "Use [TensorFlow Datasets](http://tensorflow.org/datasets) to load the cats and dogs dataset.\n",
    "\n",
    "This `tfds` package is the easiest way to load pre-defined data. If you have your own data, and are interested in importing using it with TensroFlow see [loading image data](../load_data/images.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KVh7rDVAuW8Y"
   },
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nsoic6bGuwQ-"
   },
   "source": [
    "The `tfds.load` method downloads and caches the data, and returns a `tf.data.Dataset` obejct. These obejcts provide powerful, efficient methods for manipulating data and piping it into your model.\n",
    "\n",
    "Since `\"cats_vs_dog\"` doesn't define standard splits, use the subsplit feature to divide it into (train, validation, test) with 80%, 10%, 10% of the data respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ro4oYaEmxe4r"
   },
   "outputs": [],
   "source": [
    "SPLIT_WEIGHTS = (8, 1, 1)\n",
    "splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS)\n",
    "\n",
    "(raw_train, raw_validation, raw_test), metadata = tfds.load(\n",
    "    'cats_vs_dogs', split=list(splits),\n",
    "    with_info=True, as_supervised=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o29EfE-p0g5X"
   },
   "source": [
    "The resulting `tf.data.Dataset` objects contain `(image, label)` pairs. Where the images have variable shape and 3 channels, and the label is a scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 74
    },
    "colab_type": "code",
    "id": "GIys1_zY1S9b",
    "outputId": "39ff0c0c-1c5a-4f49-e571-d0b66e65253e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<DatasetV1Adapter shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n",
      "<DatasetV1Adapter shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n",
      "<DatasetV1Adapter shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n"
     ]
    }
   ],
   "source": [
    "print(raw_train)\n",
    "print(raw_validation)\n",
    "print(raw_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yO1Q2JaW5sIy"
   },
   "source": [
    "Show the first two images and labels from the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 561
    },
    "colab_type": "code",
    "id": "K5BeQyKThC_Y",
    "outputId": "1aab9539-a2a7-40de-ccd9-47b730c52336"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UGILJOXHfkKoOFEXEG6SMussNxsWo6EwMrkaLgF2kRgeUlKibREjc4YRBpPIQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a8c53cd90>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KDKJyvJkSlMUHS6a71M0hSmUEXbekKIJ2kbwAlyum4xqtDN28gJBwSTHIuhTK\nsn39OqPplG63YDw5ghjoZDmn1tcZH14lt4nlQasJ97uWbqdgWlYs9jN8NaSbw3FVkeeaLA+MxxUx\nJFJocFqTG0PUCd1xHB+VZFkHUSWIwtlE3XiszUjRY41msV/glCUzluDHiJR436bfMU2IKdJETRMj\nUTxTH6kDUAp5V3j62SucPnkSl2nqaorLLCIeL221eak/IJOG4aQmppqkI8G3TpfkG6JSpDD7BQto\npSEqMmcIfopIg1YFkcRoMsZPI8vdjMNxycqgiw4jgiRKHznG4+yEKg7I9RRNQpkMZy1OK+rgEFVg\nOpYqNoivyFVElEMnjfKe6ANJoAw1SgshRUKoCSlBaAgRkrF4SUQDEY0iQoqoqIkp4ERomjGiHV5p\nkjSUaUq0kTp6AFKMxBiIKgetSCaSxIDSaBRaDALU4kliEWUQJcRWQEJSwBmNTh7RvhUsdERLQtPW\nMAKCyhJBJ0RBig25MSTVgHiiNCTRJB0wLnJhfYHX33EjK0sRrUtCjDzy6FNcuXpIp7sKovBNoKkq\nktRoLbhkcM5gdAeUIthDBoVnqZPhwwLRNQCsDFrJEG1I0bIVxnRdD2sKgkrEIJikye0S4xgobGhJ\nz64yqrbx08D0sEGMIaW2dlBXEVUbRARfwmQ45VBbtG4z0DLBw08/Rp5nKGVJYcSoFO594GEefSZD\nlMcYw+bmAdNJ4v/8V7+H4MlzR573eOyRazjX5V/96z8jyzVWd8mzATs7+4waePDR61zZOiYvNLlW\nnF09y5teAJ77sibhnb09DsJVpgr20yJaFNPjY65fnnA4PWTrsDVNHx4NmVRHHEVPExpCCGR5zqge\n0esVNE3zvF3NGAg+fP4vKogA8nmL2qxA1NbphDSzvwFISmRZhlYRHyKj0YgdHCcW1ljsLpLEkmxG\nt6tYXllhaXmJpWwNnefc9tefxboOt9/eZTKpGfQdT128zsLCEr1em9bceO483W6X4XBEnnUYDJYR\nKRg+conTZ25gbbUgswFfliwvrGNMzvqJdQ6OdjF5n6tbn+PCzTdx9kyPfn6ShaVEU3sab1leKyh6\nS/QWumRZxsryKucu9Fjsn2R1dYVuZ53VM3B0VBNDwfKC4+rGg4wOR9RhRHlc42shxsTW1jZaO7pd\nR1YUZHkBIjjnWvfD+jomn5A5R55ndAeOpTRgZ3hAt1jAFbP9RLKEKzTYSN41VFVNd2AZjyoSkf7i\ngL1x4NXOds/AAAAgAElEQVR33krfKfx4RNM0dDuLjIIiNxnihdwV5OKRFJBZjQCtMKoLyhIqIdMZ\nhZZ2sU6gEtRVg3MZonOiiugIJmpUVEhSJJ9aC5vVhAQhCI0kgocQoZF2cU+0BTQkEHDI7BhEkWJ7\nryEJClpyb2cTURIpJZJAnEW0iEBKJKVm51YkiUhMxJhoewIiSkWizDxX0hY2Y9JtSiNCklZTTklD\ny/Wk5EnaorUmhMja+jpFr4syEKLl0ceeZmNjG20WODw8pm4i02mFpAalQ/vQlEFrh8IAiul0ynga\nOTg6xPsaoV1hm9q3F9WpzXS0QhmNsQZrHM6CNqatTSAYZ7GZQ0SwmSPLIy7PSES8F/Jc0e32UUrR\nNA1KQbfbodPpkRL4IGjNbA5mxNC+71nWypRN7XF526QhCZyzVGUgxJoQIvt7x0gSppOSpy+WaA2S\nLNZ2ODocExvNxz/2ACIelym0gdMnLvBPf/hL57kvbxLe3SGON2icpXQJ8eAnFSrLoPTE0lM3wvbO\nDr3BhD3fUDeR8aREEuTW4ZSlrqYorTFmZlnzzfP+Y5j9oUcR0O1nz6WBrT4s6NmxSaTVojNBmzbN\n7Ha7OOuQJlE3DVoZmqbVrOu6ZtDvorOcjY0NLtx0G6PRiNfd+Ua2tvbo9zLW109y772fAuDZZ6+Q\n5wUpQVMHbjibuHT5KmXtub61S1NnZC4yORpSnYTtrT1Cgt2DLYr+AlUT2N3bQ5tj9rcqVrymrhpC\nzChTxuHhhAuDc5RlxXh8zO7ulKO9khgj2ztjJDPs7R1D6jMZG5paYYxlcljS6SiGh0OGw0PyvGBS\napaXBsQkhBiJksjzvLUCqoRRs2cH2MxgnMblBYtLC5xY7gOwurbM2qk1SmBhsaAfC7p9xXWOsFZx\n8vQqC73AK+84zyP3f4aeySg6PeqkUDhUciwNBnSLPtqX+LoiJNqXXRlChLqJpACWDKMSWgRj7Mx1\nkxA0PiqMKVBKYW2rq2sVW3eCElCutT+aDCcGYy1KZyQVW4ujSqAFtEUwYDKUihirSaqVXiK27RMQ\nhWiIs8VdjGqbBWg97FpJW5AVnve0f7Grvf0sJUElQUlbzXiu4KyYEb0SRCI+VIRY48gZjYZs7+yS\nUmJaVfiY8FHz6BPP8MwzmxjXR7BkuUHrIcY6EpHcObQCtAVRiGgktU4iY1q7ZwgBPVOaJlXTOpaU\nIYpQ14nJpKQqPXXjaTxUVcV4PKYsS1KC4XCIiDCZTBExbVOLCCFERMB73y5aSQihfTreB0QUMbYk\nbIxuewS0pvGJEBJF0cFmEW0UYFC0FlRnO61LRsGg3+Ngb4dO0WtdRSkhWKK3GMDqMRI7iGQ0dcDk\njknKvlSKA77MSdhmOYvLa4wl0niLCppqPCVf7rK+sMJxqnH5FpOyIu9ryiaRRPPU08/y2QcXeNVX\n34EkaHzrf2xtSZoQwnPB7fN4LhJWWiF8npjlCyLhNjLWWpNSoCxLjGkbLKbVBGcdUQTn2vPlWYeq\nbOjlXaC9brfb5+1vfzuHB8c88NcP8U+++93ccuttABwcjjlxotMSeONJaMrKk5LC+9aDanTCx0iM\nQl3XlE2DD0IznmIslL7GuD5VMyTLFymrGpTCWgvaUFVt4UQpTVM1xNDaAFOMGJOjlCbERFm11r9+\nb5HGL7A0WGYwWKIsD1leXeVgd0wThCbVxCRo/VxjDQz6A8bTkrouiSkwLWuGowlozWg8oZu1b+r+\n4RG2u8hkWmNsm5GMx751ejSt/nrl6h5RIlnhqKcVvYUTLPdWWMqXuHj9mHO3vIKTiwXV8SGj0RAf\nPM61YyHlWN2nTiCuQOWJGBQBjVaKLHetkwaHNR1qUURtaDAkEoHWnGKi4FMixkiMidonQmzbmINK\nOA0GQxJFVAqVdNthhyKkdrFuBIxqhVlRQlLtMWKe655TJFGINljRKJkdKyBolDVoq1HGoIxBK8Ek\nMCqhRKGx7fk1oFVL+EmhDK1s4BsmR4fsDUtGwxExCUXRo/LC1euXeeyJq0CHUDeElNA2o2oiRYxt\nUXWmZYsOM/++RimLj20kH2Ik0RbQAYxxgEJpjY7gMoe1OcpYVGzrDsZm7T3TRsjaOJqmQWbfU9qS\nvFA1kdpDWfsZ6bZ1oaqJRJlgTN5KjgLTaY1SFVoVBM+sdTlSNxXagFKuJXDReC8orYkx4IlUZSsz\niXRBzY710i6qtkBUTqebEWLTLnp8voHsS+K5F+QsLxIm04qFhRzV1IQAzirqaSCzHZyNKCKNh9G4\nor+U0+0vY9wRu/sTPv6X93LywmksOVVZElNCKUtRdAghoZQhxYi1eetPVQCtx9hmto3uVGzTzBm0\n0Rit8b7C5G1al2UZzjm01lib4ZUhJYW1bVqqlMX7QAie4+MJa6uneOqpp/n93/sjxpOSJ568yObW\nDkDbFag0IgpmVjttHNbOZBCtaIejZ44QizNtxBBSQrv2Wkq3L69SisZ7EEuIARRkWYa1DmszhIAP\nbfqolJmlz8BMj7SudSKsLi+hROGMZX1tDWXa/SoERYiCKGaWPsFZx2Chx5kbXs+n//pBjFJYVWBU\ngYQpvlbE0BKTREc1iTjdLlJNXVGV0vpoY7t3gdWR3HWppg2FzfkH3/5tLJ+6gFcdrg7H3P22t/KK\nC6dRoSRKRIxBa4U1mrIyWDvgyatX+Z7v/6cMFnOQmhQiWtqFVNucGBU+ae597Em+6dvfyflzZ4lh\nilat5uq0ofa0LoWiz7P/1X/Ld3z39xCaadtWG+s23daubdVthMODEVeuHnLXm+4G4NVfdRfOKMq6\nISZPEz0hGpLZJts8YO30abwfEuMIQutxD8lTh9A23GiNchnaKerao00kK3qYlGa2SoMWQ0Thihyh\nRtk2JZeQGI5G1EBdCUZpJEW0yXn2yhb7+/ssLJ0mBE3EoG1OCInDoyFZVlDYDEVsf8/aYIydRZ+g\nlUdEE6MgCZ7bQiHOZBIJgswao6IIKqbZ+weTSYnWCu8jReFmcosQo5DnmqYJoDUimqIweN/O+RAi\nWQZau5k00p7fGoWkdt4iEKNg7XPvREJpM3NYJYwx+CYQYoXSQpwFZlobxpMJSgkSFSKtTOZ9oCwV\ndT0F1bqolldeBh1zh4cT1HSPYx848JEQYToNHOwfk2yknq1+h4djAkOqrKCJFiNCXUY+9tFPk9t1\n6iYwYyWcc8TQen+Fz0e5AKjnVYl2AiHPE7RSqq3uz35qY8jznDzP6fZ64MG6Lo0o8lzR6XTp9weI\nc4g1KPWchzlxsH/I0dGIEIQTJ05wdNRudB9jbO3NM20wxDiLyCN55ljoF0iYME0RozTMfhoRxGhm\nxgZiaLVyUCieS1UFZwzWOrRqe/9nd8TMkNxa+NQszZV2HMYocpfjK48kDzNy7vcHpOjZPToiBGFx\ncYnT6yuMdq9RliV3vu6r+NxjTzKtA6S2cKQxLPQWsKpN41aXTlAUAwIepwHbWsSmkxIVA76KhFrT\nyXKMahcbVWSkwiFkBKMwnQ4ykwfQEdEWLx6b5RR5B6V6lMFju11cN5t1Q3oUFmN027obNRZLLZre\n8hL99VVi6GJMwkgkVxkxKpIYBv0V8o7jwi03E31FjBWIb50HWYeUDD4phofH/OFHPspb/t7XAvDW\nr/86nIUUQHQiKVA659OffgDB8Y//yXehZEoMQ0izvRlINCmRJOd/+h8/wDd8/Tdw4w0nqesDoi9R\nJkfHBCkRkmJYBh699EHe/DX3UGiPSWN2ru9xeWObUTWmDoHoFTJbaDe3DxkebRNjDYzRugNaUEnh\nsgKbtcGFMgYlCmsN2lq0brcLsFZT1wHrplhX4H3dEictmT2n8ikMPqZ2nilNSq2052PEKYMPbfNJ\nEkgzPVtmmrkSTQgt6bUZqiKEVgZqA6Q2MEmxfU9jTBiTWrlEPj+3tX6u+7Y9h1KaVr1Rs1dfzf6t\ngfb9SSSUBrRHVERUIKRW9iAp8C8DEp5OGpiMmaIoVSLSdhntD49RmaJOOTFqyioho5qqMEynwko2\noNNJPP3EZTon2t2VoCXSPM+p6/r5Vmd5XnLgeRHuuWKcQpMkYTWz/VHbAp62Gq2e2+ehO9OMAdTz\nOl6KcTY5IqJaggshcd9993FtY4dLl67gfeJf/sv/nc3t67z7vf9D60l2lqquMXZmbdJCkTmWF3qc\nPrEKcYIOHmcUBsEohSbilCN6j+kofFUjMSIxthsESCT6hhSZTb62SBRjQkTNopqI1oaUEj4ElNP4\n4IkxoFwiywAVWl+qRCJtl2IScHlG5WtOrp2k74Tm+IjHH32YM6dOMS0b+t2cs6dOsLW7x+rSAr2s\nnfjdwtDtZGAcmIAFKgc6TYgpkVuNQWNUIrMQfIOybXWcWdONMQpnDYht29qNxugcUIQ6tFYta7HG\ntHqhNrNmH9O+YF7IckdKBmfBOAuqLTKi2qwiiUKUhqRIKhForXdeAqKkPSZFUmrtaylqyrrG5hpl\n23ttJAJtQ1FSEZSQFO3ObNairaNuEmZGclpbEhGHQpkOptvhxNkzrJxaxWVLSGoIXuMAqxSYjMNJ\njQfe8PfeRK4acqn4+Ec/yeMXN9ragAgiGSlCDImt7UNCjG0WQ0PmHN43xBLyoqGqayZTTU0re7jM\ngGufdUqt42VaVlSV4IOAMmRFu8CGmJ4n4YSeXUMjrbkIY00rURjb1muswTiLigFRLfEbY0m05KyU\nxpg2u0yzhiltZu4Sbdp2fa1m72l7vRjDrL4jhJBw2awrU8ks4GAWWH0+2233l1Gtng4oNbMLPu9v\nVwiqdbT8W9/7UvBlTcJ13XZljSMMyfBB0+hEFRXiE3USgleUZaBJDV4KYrR4/3+z9+Yxl6V3fefn\n2c5yt3ervbqW3le32+0dDwneAkYee4yTwVEYoRA0ISyeMGMxYTSAhAQSGGmQjMREw2QRMJgE4zCO\nAwTcLAHb3e3e96rean+r3v2u55xnmz+ec2+9bTsjoI1kCU7rVVe973nvvXXvOb/n93y3n2Dj2h6d\n5YKN9S06tH3hoghXryHl5rDvooCGkNZFkeAJVPpw5+cppRKlI2Vy6WlFDIm0i0IiJdS1pZpVOCkx\nZYlvu9rVlTVOnT7NeFxz4eJlbr3jVm44fQyAt77zrWit2bi2SYzQ7QxYHk3Y29vC1RV725vgJ7h6\nhu70ybQgkxFJcgYGFyiyHKM0/U4PIxS5NnhvSDECPmmGXcLV/D7nYIxzSEFgsowsF+hMomQkLyQi\nJEZda4HDp+5FZ0hjsNOa8WhKfrpgNt3hhhuO4bzHFIa3v+3NGK2QpuTFl1/gxPE1+nlaYBQziBkS\nMEoRhEVFixKOQIOINYcOKCajTYy0beCKRIaAIKKcp0AinUe03UuiXkDGSKFyMqGQTYO2DuMUUYVE\n8oukdtEIVIDZrELaiHYeUTdILYjCJSxUt+c7CAS0Scl1sbG4kLbMIgqQAo1KRh5NixumBkDlmsZ7\nspa58ukCIwpBkOBExOQZRgkCot2GpzVUSZXO0QInIVOKJgRUXkDLU0QBVkTqAGiFCwm+cz7dRzGm\nnZJ3LDiGolOSFx1cmIEEbTJUSFCC1hqhxPWdotCAxLuIEKGNghRIqYHAdDrF2jqZq4DxeNoKj5J7\nNMRIUztq21A3FoSiaRxNtNS1xRjLbFrRNA3OhiSNsy7hwyJ9qt6HlneQaUcndVvo2yZC6bS4tg2U\nbHe+Rue44NoGJF3zSiX2MxKIwRNi2xELQQgS0TZnYQGxSFK5TGRkFID4xgTXf1MXYefBoNEKVFBI\npYkKoi6pwxTrIlpnuJiY0NAEjOkwHk3oLyskOVpIbE3SpEpJlhmscwlyaLHVlP7TPqlo5WoibU9C\njC3bOj8hfbjet95+51IGgrqOlcW2tmmVEUh5F86mbdrecMjZF1/k3PnzVLXnlVde4eKFVwF46cUX\nOHL4CLs7m+R5hyJPLHuMjk6WoWW64Pq9DkvdDv7AKmsrA6pql7w3oDDrrCwNWF4qKQQs9cG7huBz\ndBHwTdV2Em22hpAYLdFKkWUarROxYvIOQVUIIMSGQX9AmRumE4vKA8PKEWna8w0CTVkU7O3tUI32\ncLMh3kXKwQo+Dhl0NE4YZrMZvZ5GxhR8ok2gyFPnXRhDU1mgIssCCE9ZwFvuvY/cBBA2PZ+QKJ8W\nSeMD2nuunLuAoMaJgFMpi9gIDT5Hyy6hqti5usF0JJDKpxtR6vYekiiR4SxoL6j2RkzKDJNJPBZh\nBCiFFAYhMrTKsdZiraW2DTHaJBhoZY1JHiDT9YEkL5MGPMTk/hSBtrAoEEkDi1RolUJ5HIHYEnJJ\n/JbSAIejUWoOpGRSjTFGJkhDJMgpilT0+0vpuo0iLbJSK5TMiN5hvcfaQIxJYmZMxqyZJVJMpOtU\naoOWEh8dEJBKoYVEtWSZ0hGlDKrtaKsmxUNmeY5uryGAzBQtpAAKSYzT1H16nUxSIj2etU1LNkoa\n52icbRevSGMtzgdcCITgGU1SAFTTWGrrqJqGGCNKthyOC9S1JYS063AWmsYmotvWRFLWjPfpGvDe\nJ+iPiA+hNb6kuiPkHK4AFwQBSbLMgCDZ9MM3aDrcN3URbnzkwPJR5GgXnRs2xR4jmTE0OX42ZTqZ\noI1gGKukTGhSiI7II2Mv0FYi3QwRQIoCYoq/TMlrGd5KclOSgnhCsitLh1QFdV1zoJ8ndUUVkSqi\ntUEZQww1uZTIaU3uAjtXr3Lp/DqoAmsldug5+8IZxuM9vIATN51GCZ8cTgQmVUWd9nNsXdsi+lSU\nsjihqwKrnTyxym7C2krOxfORtdUV+l3PdDTl2PHjZNKwMjD0ByWKDjHLybTn+NEuS31NZ03j7ZQy\n66JNirrMdSA3kU6RVBbB1UhRkSEpjSUXnm4mCVh6y32ulBcZ7e3Rv+0Y/Z7G6CEqH+Cx1NNzlCXg\nA8prVJDs7V1CiBl7ux4RC8LeRYSsuenYUVAlufI8d+ZFjh5NnnuRS7qFwg736NGhokbJ5Gjr9nNi\nmLG2UiJlYFzV6GyAEwaDROGR0fLkk4/zvve+G6mTqtbhkEoSgyQ4QZEvobolSwcPs7TUZTrbpcg1\n48mIstNnOq3QJse5SMwcNY6oJJN6hmuqVDijZnc4odMbsF5v0FSe5555HtuMgRpnK7RRKGGoLGR5\nh8uXrlJNKx59+Cvc/qbv4aWnnqHslC2OqdAqB2HYXd+gK2Hz0nmUStdIp+gBEalbjFJbOmQUQcG0\nQbXyuZpArjJkDNR1Q0fm6CDRXhJc0jDbymF9YGIFu1YSyKhcACGpZxVC+gQtSI2SiujSDkGhiL5V\ndCiBC57ofCL/XHJdxiAJHoxJhJdAYlvoL4hUoGS7IAgpqF2NMZrJzGJMSw5HiRRTtMrITJEkkZOK\nouiRZzk6RibTmqLokOed1u2ZOlSlMuZQg42eiCJEg5AZLngqV4MWzJqaEDShEYQALoD1ATetW6lp\n6p4bN6Nqpigt2qIcKYqCvOgwGo2omrpVRlmcDUzrvwGYsHMN1axBhojwDUbDNHiGswqmNc62q5n0\neAcQkSIlS7lgqOuQil9ooz91wBjDeGIRCXFP2/B9C1okkPafYF2grmuKXLd5FR6PxLZkmVCSzd1t\nVlcO0j+4CrJkOnWYrmBmLTYKxrMxX/zSl5jNKpZiYqmPL62lrgONMYpum6I26JUsDzq4xiGVRuqU\nMmWyjKNHj7DUhzI7yHBng+g848kuUjk63RzykoggRMugN2B3+woHVpaQwiXBushASLJORpZLup0c\no5eIoYNRkiKXFLmhLEyS+tmmJUBCyzIbtJF0ZIft3V2Ct4DHO4urG5rZhMnkMmsHugghyLIshQLJ\nRN5sbq0TQ8bb3vp3OHHqMABCaKQQaCL9jiHIkqy3xri6wKFDy5w+tcZ4uEtnpU+mMqwXIDVSqqTj\nbG+UKEhWZgGmyHA+IExi21WuqZ1FZRmVdRS9AbYZ0+n3sY2n1x9gfUDlijpI+itL6Dyj7BYMBiUi\nBLyNHDmmmdWepaU1stzwxvveyHi4BdR0igwpBNZ5PIay0+O57lkGX3yQu+++G4C77rqLpqkwWU4I\nEonGe8WrL19gMtpjZWUZ52Y0tkJLSfCB6CM+OIgNzlmC9zRVTYhTIhZvSqbVGB0DtgnMrERFEmkb\nU7cd50lsQWBjIsZcu1UL3lI3M7JO0cJNdqGhdtajRSoP853hnLidk7iBRCZb67HWEYJL2SNAXdct\nvne9K/fep87WOaSUzKZJLhlC+l7T2HSO9clQRVIJtf6VFnpIMIFSLTRAkuMBSK2Q2iRc2DlirJBK\norO5nlcQfEApneCW9t+UtMcBrQVay7QbEAnndy5BLTEmiNH7QNMAEaz7G1CEU4UMDAZdVJAIKfDW\nU1cNys23BS1uGxKmqYXBt777qqoosuTtcd6jgqAsS65t7LR64PRfC88tPmQBCXJwAud9G9STVAOz\npqJTgDKSsat56KnHmUxrXj13haKzwmjUcHHP8vCTz/Olxx4jCo8nkJcl4/GIkyePIrXiyJEDnHtl\nC5Npulkb8rLcTyORygylDY2r6fa7mEzzQ//Tj6D1BDfdws5GPPLgI7zwwgt827u/ld29TW6+/X4+\n/58fpNfrEWMkyxIBmYKOzOLC9cFT1zVNU1EWAiUNuVYsL/cpC8Opk8co+0doouXSuUsUWc7O1g5K\nVDg/I8iMalYRYyQ3ijyTZFrSLTKmtURLT9SaXsdgbSAzkuPHjrG6qrmy4fj2932I++5/AwB33nEv\nw6sXWVkdkGeKnjLkqkdwniIzKAHeOna2dshNgbcSLeca4CRBcs5RFCX/97/5Fb77H343Z8+c4YWX\nXuGee+/j6KHjWO/Ym4x5+vlnecv9b8K5iiDAKElWaLwPTKoZX3n0MXxs0Jmh7PdQBB577DFGoyG3\n3Hgbh44eJy9ynn3heWZNzeX1KywNCrQumFQTRIxkRQcfAqPJjCBlMnXoFjdUiqwoiQK0MkiZIYMk\nCokXkBUF0kWkEeSmSB2fBhNA6w51XWOyjKLbwWiJp8EKQ9bT9POM7a0h2xeuLsbseO9R0i8kiKmz\nS9d58AFJ2pY3TYMuMlx0WOswJsnAkizLJrepnN8fiTUxJkvmCJFkmnMz0/5JaSKxXqkjEvMYWdGS\nwQlzds6htW6VC61DtV1YU3a4QygWfMq8WDZNskYnbiO+5rnnrzlpuv3iceaxuKGFIxIRLRcDSecL\nQ2yVFjEmcpFWWeRsIgQXgioB++00r+f4pi7CQkKeK5ZXethRtYAMnIvoKFEqQ8qGmNzz6c2JSXal\ntSLYCbJNLokhgIBut0tVXSHrtKB+iMnWuU8tQUzMf8LbEhEn04O3IvLIrKlxAqrgmHpLLQSDQZ/h\n7lWsAa8NHk93kONdQ5YbOmWXQ4eP473i5LGT1LNHWDu4Rq+bOpMTJ09Qmi7TmUVqze7eDqboMZo+\nx7/+1X+DESMyZck17G3tMZlVPP3ss+wOtxDFKoNBn3e/+908/diXOXjwIM1sjNaKPC+SrhRNg0Qr\nQbdTsLZqyIwA36HXN/R6MB47ok/B+XXlUFIyGo0QYkpRKhrXYK1ladCn1yvp9xy7uqbIFSYvUTT4\nCKUWZFLS6WbU9YQ77nw7X3r4DGVnhbxNFrvx9CnONbsobyBWaDTToDAKdMsidYuSye4OBIHw+xyO\nXA+4qaqKqqr50z/9LzSh4uGvPMz7vuMDNLWnkDIRZplpO8Dkdqy9QwtNb7DEQ48/jtAaoZLWumpq\nPvPvf5Nnn36CH/3nH+fXPv3/8KEPf5RnnjvLpctX2RmO+cKf/DF/7/3fRr+XUbuGsijxkBLqVEZl\nLbWDaZ0KxrSpicFRtNGWkYh1jqa1WNvoscG18a0ucRit+UMqgY2BaCReJImUD4Gy0yHOLHXTkLK2\n/cJMNJdXhuAX71lSAki0TvLEzKTdh9Ya1XaVqUNM+ve580wp0erSWXAfzjVJJeM9vi2O1z+brz3m\ni8MCi20loPPCuggrWjynun7evq/5YxljFn+fG7CMMQvCPb3O65N5vE8Ie4xJFTR/vW6+SLWvzznH\ndDbFuVR8iyIDkiIntLZxkRr0r+tj/Ksc39RFWApQIqIUNPUM0Zbb6CLWhiTAjumCC16gompZ4JgY\nZBeS3TSk1W6ujrDOkgvzVc8mFgV4scC173JoBy6GEIhNTd7RmAgdnTEmZRhsbWyxuVNR9lZpLNRB\nY33EDiesrS0xm47pdHt86csPcvTQMV54/hzjaeDCxfMIMQHg0cce5dCBo2xu7NHp9ahsa8M1gocf\nfogDa5pqvElpFLnKaCrLrJmxN9zi4vqY0Wia9MXOsVdNKYxkHumZRjwlR1XwkaapmU5rKmEptGJ3\nuEETFFUtcNEh84LpxKK1xrvEXOdZn+FwhK09ZVlC9O2kEk+eSbr9ZZzbZW9aYesxk/EutpHYTs3S\ncoGUlnpvi+nWNbrHDqGEQ6gGa0eoaHFeMKtTfKi1Dd4WjHbHGDSuDkhZEENbUPbd7GVZEELg4qUL\nmFyytLyE857f/Pe/xYnjp5jOZpw5e5ZXz73C3ffcyiOPfInv/M4PsDxYYXtvl/WrV3nnt/w3zKrI\nrKo4dvQI3/UP/j5Ey8lTp7j1jjs4f/ECJ288zTMvnEVlMhVPJTl34TzBVVy+tM59978VoTIuX71C\n4zxBBrbbceiNTa6/WV2jFclZpiRSC6TRaYFQqbFIpoJIEBEXQpoGopNTrgkO0Y7vappmcQNLqRbF\nS5uE7yqpF0UNEtkkpUyqGOeIWdqWJ6JSkWXZYjc471DTRJq0Nd9f0DqdDt5FqqpexMhKqRcEtpRy\nLklqzTypwIeWIFdSkulsca5qTUfz7nX+5/1ddMJjUxHXWr9muOb8NcT2+gcWXfLiDpepWdNaLf59\nYT4AooUmEtxwXTUk5tj24lxQrVpK6W9M+fzG0Ht/TYdSkqWlAYJIp5MRY0CKZGIQEQghxRYqhRTt\nVlc0+soAACAASURBVKJ1uKVVMAIhOW2UasccGeratm96ushiiCnhyvsUKiLThWa0YjKZEIInyw3e\neXJpiLOaLAqUiywVPUqZ0dEFvazEzyzdIrY4s6TbG+B8pChLZrMZ/W7ZXoTJNdRYTxJVQZ7nCKFw\nPlBVKVikqpt21fZoJRe7oBiT00ggCD4ynU6ZzSz/7+c+T1VVOGcXncF8KzfvWGIAEdPIKIkget92\nR5HpdIQgYOsGYwSjvRESQa/sIqVgb3cXZy1aaZTWTGczQrAURU6/36HIU3fdVGOMBtdMmFbbPP7k\nF1lff5Vz55/mzJlHABiNroGoUTog1VyL3VpuhWw1s3PRvSKG69vepAO1i27pwoUL3HrLbYwnM1ZX\nV/HOceDAAXZ3d1GtsWZtbY3nn3se7xMkYxtLURScPXOGBx74I5TybaEIdMrUsVZVxdNPP83O7i6X\nLl1iY2MDH+GRRx/h0uVLPPTwQzx/5iyPP/UEv/M7v8MLL5xhOBzx9DPPcPHyZX7rt38bgP/8hT/k\nwoUL/MEX/pBzFy4gtMQUOVVj6fZ7uOCRRtN4i5jrXYVAyZQpMa1mCV4rSoJIOzK43tV57yiKgrIo\n8M4n2WQbECSVotfrtZBCwtIzkwqubZoEyQnBZDJZmJGm0+lCRZCGIsTW7p9e23Q6xbm2c+e1nSS0\njcu+znUBM7RuTrkwBs3x2OtFdX6+Maa9rxNBNocOaGVrWuv2nhE0jVt073MX67ygf/VjzRcla+1r\num1I788cmoT0WPPMirQoyQXUEhZxgK/v+KYuwrNpIM+KVITLAq0SvmMkmCwF8hiVSAxBIiQyrVPx\njZYilwwGOVpFnLUomW7Gpvna2VD78eD9AT7zbZyS6eY3WpNJjUaiI0gXE5PsIrnM6ZoO88CpQmdp\n8KGPGGkwKkMJjZYqaWzbzsfk7Xgjlbz0WufJ2RVTIYo+pE6HFPASvIO5q83HNjkr7RzmN4sxGatr\naywvLy9W/TR5xGBMSrLSUlNkBcYY8pbQMEqjWpwvia1YvCeyhXW8SzAFQqCMTs4noynLAiEj3V6H\nXr9Lt1swWOpx6PAal6+cw7oJX/jC7/DZ//CrAFy+/CpSJNKlcVDVgary7VYwLTI2hEU+gdIZou12\nRAsPSSlpGkun0+HQoQO8/33vQUR4+KGH8NYyGY7oFgWDXp/Na9e4cuES973hXgqTkZuMzavXuOfu\ne3jnO95OpyiwTZOchxEyY9i4toF3jtWVFaSU3HHHHYgouP2221oyqWGp3+db3v5ODh44yPr6Os89\n9zy9bo/gLPfecy8A73/P+3ji8Sfolh1Wl1doqprR3hBBIPpApyjx1lFkOcH7+QXZdv3pApXtAtSO\nSXzN1t/7VCTY1/nNd3SylVjOi5BSaiFTLMsyff55TqfTWRSYwWCwONeYbAHPJVWDXhS6eXc8L3bz\n1+ScSyoLpRbFO8aIbZKELBG29aKznhdMYHG9WmtxbXPknFtgwGnQgl4U8AQ9iEWR3g9TzGGJuTmj\nKIrXdL5y32KWin9y3hkjyTIFhEVh3v8lRTJ0fCOO191Pv+c976EoisUq9IlPfIJ3vetdPP744/zU\nT/0UdV1z/PhxPvnJT7K6+pcbB2IUVLOKbkcyGe7RLTVbowalIkZI6ta+qGVst0yOxIA2CJnUBkeP\nrLC5vk5VVSjhmc1mi9X66x5zkL/VEfuQ4gyTgy3io8OJpNcMEtKQNEE1q0DVmHYqtAwRJQVatEHh\nEmwbn9jkqdDEAFVtMSZdfGmEtkYok758IgtSjqynmk7xjUOQ5rBpqVBRpACvmNK4MpO6g/F4l0sX\nLyZMUKQIP6LH5B3KrMSoDCkVeW6INiPkJUVhGQwUQneY1ikFTLUXbPQBIpRFgdQ5dhhQRpOVJUpr\nbIhUriEv8rTgUBLbkHKP4cCBFbQCSQ1hCkA9q/GVItSGLDctTaoSWRUFDsFkVlNqjclzmpA6qfQx\nJQ132skE/sk/+T66gy5lL+ejh48QhGbj6jZHDh/n4Qe/wrve8U6UhJ3dq4wnO2lwo0/pYN/ytrfj\nhaTf7dLvdAnOY+uKg6sHOPPCC/zgD/wzauuZNYGXXz5Pr1vQzGqWen1kFLjGcse9t/O5//j7nLrp\nVi5d3iQ4yE2P9ctXAHjoyw8hYqSeVZw/9yqXLqxz88138MhDj1MUgt2tbcquSTkQ7HMOQeIoQmoA\nEi6WHINze7sQ++AC5iaj9ABz7FPKBFP4JjUyhUyYal3XZCrHS9+SnGZBal8nvGJb5EyrZPBkWbHQ\n0c4Lcrr/EsyX53lrBU5/jqNJgjbcdbjB5ElzPZ+cDteT0tJrlgsiL8b4NYX6evc6N1yJRTcthMDa\ntBtcLALhOga9H8Oed97zTlcZjRQ6de0haYnL4rrCgtYtGr/umLS//PG6i7AQgk996lPcfPPNr/n+\nj/3Yj/FzP/dzvOlNb+KXf/mX+YVf+AV+9md/9i/12MbkJDeMIwSPyTRa1WgNuUkSFWFrUo6ownuZ\nOkQRKYuCI0d7nDx5hOefuYJUkizPMEa32QpffcwHhu67AWLrnhMSrRLcYUWkkZEoA42MWBGxMY0i\nCnWNJoUN+aqmcVMy0WUyHWILgxCKatbQlCljlSgZDic0rbZyZ3uPTHcZj2cY47HepvwIH4kupAI8\nz4916fsp4D3QhmVRNxbnUojNrKpIzhFB4yLW1ehCMp0kh1u3I4ldgwpteIpLxGf0bXxnvM5YJ4bY\ntpIky/b2Hr4U7I7HOATrG9cYzXbJspp6HChkD4FDSEsTCoQc0zQwHA6JMU0S2d2ZoEOOnUXKXqSK\nnqktsV5Qu5AWL2+xdc20sphOuQ+OoGWqU0dz6NChZDCI6aafTRtuPHkKZyOlyYjOE7VkdWWVIweX\nUz6BD6wur1LVFqRiNKxQSU9Ft+zwge/4DmbVFCENyyojCMMb7rqbJ594gne+4x0cPXKED33wg+SZ\nIs9KPvpdHwVhuP32ezhz9mXOv3qRj/33/xCAd33Lu1ha6nJl/SKDwSqD7gp51uX0qRuwtkpdo5C4\nJhVFRFyY4IVMBLISqjV7kLpkXtsJi3mbRluYuL61F0phtFnYiRFzgj9d83PIYV6UmqZZFCshIjHK\nRTEzxjAej9HKtIthWOzA5kRgmKsxCPhWErffpSrldVfafux2f6Gdn6uUagOsrjve5t23MckEJAT7\n4Ape0+0uvi9FKzeLCzIvOWGvd85aa0SMKZBJCOo6TeSRi4e+fu4cEnq9x+t+lP14yvx46qmnyPOc\nN73pTQB87GMf43d/93f/0o/to6Mocpx1DLrd9GZIyBQUJtLtSKSOOCHQCnp5u51WmjwXHFrtsnpo\nhbqBqrEUhWZ5ZYmkD5ZAAOUJQhAwKR9ABsBBFCih04BHERGq/TcKARJ8y27XzlGHpBlO8+1cCo6O\ngllt8VFgm4CtU/xkCK0nXUtMliOCwtZp+kLK/g00dRoVFILHNmmmXmYMnbxDr+yhTXqtQiafu5RJ\nDyoi9DspVKjf69LvdxgMuuS5SVhtJtLIpHZLF4IjBZ2k0BLfBKKLBNsQvEvFP7RbRZVsos46bN0w\nnU6pJhXVZIoSkdlowt7mlNmeY7o3xdU1mc4wsqSX9Shkh37PUFWWQW8JgNwUZEVB2e9jyh4qK5Pz\nSqZcjF63x+133sUtt92OyTMQgSgjUbS2ZZKlt2osHvAiUDUzRAwcOXCAqhpSFun9b6ZTOsrR1x47\n3iJHkAWIrqbTz5k2FUpFbNNgtMPaIbauMBh6mUYzYzq6xsc++mFKnfG2t7yZvZ1rDPpdRJQEHxeZ\nu0uDPtE7VAxMxntAshDPpmMOHTyMFJIbT9/IiVMnKbtdbr7lZrIsg5hmxYk2pyJZNQIiNkiftcaH\nGckb12p4ZWvpDYBUCCVS2AyBue02hrSdVi15Z5Roo0slnU4n5Wpkhl6vl4g9pRj0+wsiu8hyjNaJ\n0MrSjrDbLSnK6zxNMlvHRZ6CbyEVrdPwWinSczprW4ncPJxdLHTC89I7TyV01uLbhT94TwwB59Lk\n9DiPFoB95pG2aw5hsRMQLcA7L7x1nXKO58oJreea4eswhvcO7ywxBoiBzCQ4R8l9/xcJBv1GHN8Q\neu8Tn/gEMUbe/OY386M/+qNcuXKF48ePL36+srICpC5oMBj8hR9XSI91Nd5adN4hUwYjIkuDjE6s\n8FEhdaRxSdbUiw6R9QGFDA0HB5qszBAqhaQ4XyEJNLNAsIDwSAPOCbToEIRFKAcxEGNBZgomoxlS\nNogYEChyFF2lk/i8sXhnqWfJDuxd+lAyIxjXgSgLsmKAGE3QJqdqahrvmdmKcVMTvcC7QG7SVsdo\ngwopRyE3mmnTYDJFQNLUDqvBVzMab4m9AisiJnqk1mSZSQE9RAgOnQmCrzFaoVWS2pReUDlFpg1F\nqcg7aSp14zxKO0TQZFG1cbECJRVZliPQzCqLKbOUkesjS0tLDIpl+mZCwx651AhXojwUMpAbRSlz\nqmnDeLhNuXwQ20S63RViSFvPfrfAC6idxUuFljk9naODY6nQHD6wxomTJ1i/eCHh8UrgpENj0AKM\nUEgkOs+oYk3jGrqlQdvAZHuDleUuLkxx08ju+iUml7fJxIxOz5APbidgkMsKFwN1aJhZcKGhrq5R\nVUOK7AiakosvP0tvRXD10gYbl69iEIx2N1lezqjqIaEuEtRlU7atdx5na6SILA/S1JQQ6nbLLcjz\ngmnt8SFSO4fpZGSdPGmsfaSpEzFofUNuJLN6TDPVWDfDFBCjZDZT6LwNmQoK75M5o+hkeJpkkPCJ\nBCNGZIyp2EoWRVCKZDvW0qC0wtpkYhJSXt8N+TSGJDnLHEIl+Cd4gZYCYiIzjRLIfT1dJ0+KFSWT\nLT65VgW5NmRtZ9rrdhmPx+RZtsgh9s4lvDWmPBQfKjIlid6RFzm2rlLmr3dIabB1lbJTlIIQU0yB\nS6YKLSQipK52nusyD32fd9zW2kWnPMeHZRLztaaXtHhFH/ZBlQIhY8oL+QYcr7sI/8Zv/AaHDx/G\nWsvP/MzP8NM//dO8//3v/5rz/mv6weFwyHA4fM33lFIcPXqUEARSGhoXKfolRmcQE0xRKJXMANqC\n9W2RDBA91latgNy34HpKM4OE00pI5IBIjiQp42Ibl/ZQMU1eaMXdxISJxRCo64pcN0RAaY3SEaMD\nShlmVY0xOUJJoox467Ct5nY6degsmUvyPMM1sSUabeqCIBFiMgWsz5+3aWzSidoUzzedzdC5YTqb\n4qMkNwbfeITxeA9V1TAzKd4wuApvFNY6ciRCGjwuaVRtCp+JviJTJUWRIyL4IJFZhs7yNPEhRKJK\n+uAEC6d0rxBZTNVIMiKBkhlKRoQ2KKnY2tlh/eIGRw4fpqprQohUdU1tE25e1zVRpHl2UhsiiTzR\nKn0WL730EmfPPMmRA2stZpcyc4mSIBUu+sVkikIoXnnuLAd6JYNul4uXLjN1FXVT0+zt8epTT9OX\nY6QbIURNt7fLSGfc+tbb6GZrSBHQAna3NuhlgvPnXkLKde649R4e+KP/yGDVUFuB4DBXr13j137t\nX/PBD38rRw8fw9qQcgViCtMRSqGMIcjIZJbwbxc8RiQTBMIjVCKIXDv8M0RonMfbGi0K6mpEY/dQ\neYrcjDGpYxo7xTuJMV1ivA6rze+uhJW2BUXKtkv0LRGXkuQQpNl+pI41V/kCtijLNIhAoJJjzDYo\nLRECbGMxRdZCD7EV9bTwmPfpueJ1lUSMibuw1i5qQKphcgEH7CfzgIVSYuFka+VocxXEvHPeT6qF\nEBY/m3e3+6Vui/+35+4n8Obw1n7VhG4XhPSz/e/u9d+Z4+8AV65ceY1cDhKx+RdtOEX8r1XHv8Jx\n5swZfvAHf5Bf/MVf5Md//Mf53Oc+B8D29jbvfe97eeyxx77mdz71qU/xS7/0S6/53vHjx3nggQe+\nUS/rb4+/Pf72+Nvjr+14z3vew6VLl17zvR/+4R/mR37kR/5Cv/+6OuHZLM0n6/XSzLDPf/7z3HXX\nXdx9993Udc2jjz7K/fffz6c//Wk+8IEPfN3H+N7v/V4+8pGPvOZ785XqfW+/hWM9y6AQLB05xplL\nmzz1wiucPHSQ4ysdBILnz22yPm04uazpxJpdX7Kxs8e9t/R58xsOs3b6Nv6vX/o9/ukPfZhjJ/ts\nbUo++XO/xtETR7i6cYm6ytIIGimY2UCQFh9hYAacPrzCu95xmre94xZ+/wtf5g8eeIGf+hffw9pS\nwAXHzu4eO8MpTz37Mg8//AqHjhxhe7vi8uVdVldzcuM5eniFpp4wm1U0lSMrDEqVVHXkytUxqysD\nel3BHz+yw3vfrDi0doSt7RF5kTOZjQhKcf5yw503nuDAQLCzeQGTGVRREoViud+lnowxRZ8vP/Iy\nb3nTLfS7kl4p8LZqJxZETF7gg6Am8uX/cp6bbj7EqZszpM9pZgYfrrVRhwph+jSyw0MPPsmdt96I\nd1N6fU3RUbzy6gWE6rG703Dw8EFefvlVqonj5A0n8c2QQwe77O2ss7q8TDW1bG3scvrUSSYOXjx/\nhZWDqxw9uMSvfOZ5PvGPbkIbw6xxCJ1hPQhynnj8LCdPHeTEDYdQssbOpthZg1ZdfuB/+V/xUVBm\nHf7Fj//vfNv738373vt32Lp4jtmFC2y//CKFENTOsTPdQ+aG//TvHucj3/mtLBc1sd4liIogT/Cq\n9Lz7778f1S9Z3xjxv/3Yz/Ld3/V3mQxf4LbbTvOnf/Ys516t+YF/+u08/9Lj1LXmoQfPc/7ihJtu\nO8Zb3nY7b7n/fg6v3UjlmpQ2q3MaF/jiFx/mwYce4Yf+2T/m297/P/PAf/opgrco1cMFQRQZJiv5\nzGc/y5133sbdd96KpCHTUEhJNd7goUf/jOl0yomTp/k/f/FPufn2ZVaPBo4cPsV7vu0jhMyjg8IE\nzZXLGzgkv/7b/46P//MfQrga5Ro+/W9/k5dfvsJMalzRYVZHrly6hgqC5UGPytWILKl/cp0vMiea\nyrK+vs7xo4cJwaK0xAZHVmYEF4lBoYRia2uLLDP0OgXWNcToeeSJDd563w1thysYTSbUtWVlbZXJ\neMpwNObg4UNoBKPRiLquWV1dJcbI1tYWZVnS7XZb7fsMHyLLy0tkWcZwOGQ0GnHgwIGFaSPNphOs\nrKwscOaqqqjrOmHe885YKoajEcaYhf53TsxNJhOKolioJCB1xLNZIpHn3e+8E1Yq4+DBozz55HP8\n+q//+tfthP+ix+sqwpubm3z84x9fsKg333wzP/mTP4kQgp//+Z/nJ37iJ2iahhtuuIFPfvKTX/cx\n/v/a9to5nj97HoMge+UatU7BrnujIUdXOgjnkTHSLXOKQpAHj6xASMWp0zdy8tQR6C/hI8lD3xJZ\nWhuIKZwjbafSGCIpZZvYn6QyUsnFpOYUWpJ0lo21RBHZ2tnh0vomPniOHVvhltvuYv3qHrPqBZZX\netx4+jiZsihZc2Btma3NTZaWDzGbOqo6ItWrHL/hBIN+2tjceeedZKpg7cARlM7Y3t2kv7LKxs4z\nHDh8iIM90LKmv9RnPKtRWUknz8mkQOguWS5xIQKSxiYyQ1rVssqtI6iu08BS72lsgwwK71MmstEG\nRU5UXZpaorWgKEqGe2O0TmNk8rJDXUu6vT4myzh+/Dgvv3iepaUlMtOjkweMDpQmJ9ORzPRReU6v\nW1D7Cwih6XQSTpqZnACUZRdPxGSGpkm2dKWStEhqMDpjNBtz4EAawy5COw253SZqrbl04QI7L57h\nkNI0W3sMig4xGrwXGASzyYTlfoHQBUW3y/ZYkWcdyk4PrwXb29vYyuGqmlM3nKBuZvS6GYcOBra3\nhrz04gYnTtzELTffw6vnHmd5cJjtrTFC5EynjsrNqJyl7A5onKeqE9E3h7nGkzGdImc0HqFUTu0a\nTOYZj8Y4a7G2QdJQT2tGzZQ//5Pf4+rmZQ4eXOUPfv/3mE4Ca2s3snHtRU4ev5EYE7w2V0jM1RAJ\nd27lXAvlQVzABHN9bdbmiEQSbhujx3uLkhkhJNeilNA0dSKmZcTaGmEkIgiqWUOn6KQsYK1TZoNt\nECLxInVdt5DBdZ3wHHpQSqXbrrUtL1x7sDh3PjpsOpuhdVJHzPMe5nDLXP0wzxiOcxy5VfAkjXPi\nH5RSKWDJ+wX8t1+PDLwGmvhqyGIOeVw/L8wdHRw9evQvUTW/9nhdRfjEiRN89rOf/bo/u++++xZw\nxF/16HQ73HPqbg6udNgaT7mw3bAxehVP5NyFixQmo7c84Py5Ld79tjdSCssxBjz82FPcdMvtnLrx\nMOszj85LXBREaVCZxvnkvU/TUSRpYHiK2yMk7NcFt09Qnvz2uhWqS+WRSrYpUsndduXaDqPZE4xG\njr29ET54hns7nDixQqYammZEcCnbdDR2bFzb4erVCd478jxhe8888yzHDh9nc2uIyQqG4z3ixXWa\nxnHl6jqxNuxubDEcj3BCE8WYtaU+0+EOyrjW3WSpc4FrkpSPxqK1wYWaiMLG1gjhPJNJg/ABX3uy\nvMGqhCULJamdpq486+tX2N2+RojLdLoZ19Y30FkfQSftHqoK5wJVU2EbT26yFBcYRTvptkgzxmKK\nSopE5qRyluVpokJWUDkHItleyzLNAuz1BmjZYKspQrRxibK9MWSEEFFSsjcccsddd/FnL55l6iwr\nnS5+ainLkhEWoXNclrMbAt3lAZfHV5mGkvf+vQ/ioid4BaRM3ZXlNYp8THewTLc3Yv1KxZNPnKXf\nHbB+ZZdXX25Y6h9gtGdp3B6TUU0vF+zs7CKNYlY7fBSMhkPqyrG9tQVAXVVMR0OqKmVeS13Q1JZq\nOmEyHHLtymX6vRwlPNfOn0MguP+N9+BjoNPp8szDz/PS2bMcOh5ZXRlwbX2dgycPJ+eiSI5RokCb\nDLguG4O5Htbvk2K10i0pyFRKDFTGoEXCnwU6DTjVKX874jBGgS5AzqfK6Da/wS+KV5ZlCxdZKnDX\n5V/W+sVCMc+0mKOq89+fF8V56M78a/73BXEmXytnm0+tWThD9+HBc1nb3Nxz/bVdJ+j2hwbNseb5\n780747kx5bpETmLM34Rpy7niQx/5b3nr/XcxtYFLOxX/48d/lO///u9n58KLPPylB3nh3CVUpjj7\nyot8x995J29993fx51/5BDfdeg+nbj7ADd01ML/FzXfcw6GjJTu7m+iioHahLQ5ATGHNc51wusHb\nCRot8ZRlOSEErPU0jSWIFBgdWnFOyp5IH06WF8SoqeoJSmmyQmCMwAtQypOZSF4IylIwWOqTZ2kl\nljIFDG3vpN/Ls5KpbVCZSTK2mKXwFO8ReY6zSToGsiWEkkzOxyTh6XZLgnet20lhfWynIRggkW1Y\njwxpNLwXYBuHMoJxFRHRE5xndWWJwwcPYHLFlctXKbsdNq5N8N5z5epVPIHdvRG2nqDVChtX1zm0\ndojNazt0yi6moxhVllntmVQV2+1MvfUrV9Ok6sahMkNtI1Awmzq2t7aJYYaWDYVJYS7K6BSy3pZz\nQkAhKfKCIALf/pH/jgc+9zuMd3Y40l8jdDOmzQQ/GKAOHuLS6Byzi3vsVNv8o//hH0OeE7Wlsg2D\nwSrea86eOYdrznHbXTdz7NjNPPPkOpcvbTCpRwxHhgOrNzOZbDIcTvnge95J3cy4fPkC6xuX2dje\nJKI5fvI0k9GYIjcsD5IcT0TYWL9GVVl6g2WmU8fqgcMs93sUmUpDQMdDZtM9hrtDqmnDxQtX2Nzc\nwPmAkor73vgGhtMzPPTQg7zvfSfY2tpitb+KkGkb7Xwgazu/uS3fB7/QVIfg0mTs9meIiA8eJRQh\npDChXpa3KXnpHgwhWaClFAhPG6fGYhu/n6hKxfF61sKcfJufk2UZ1Sy55EyWIUP6/TzPFx0spCI5\n72AzY1BaUxRFmsQc46LAXp+JGF7z5/nzz1/f/i57blPe353PdfDzTnc/4QdtF9121tc1zPFrIIi/\ncp37hjzKX9PhfMP27ibnL7yKKrvULo1SCTg+/F0f4kMf/AB//shz/Py//BXecO+9PPrEM/yrzzzA\n1Ep+5d/+Kt/61jspj5zCBsm5i1cQ+SqNj6isoHZ1m1UqiVKm/IjgkTptbSBt7ebDNrXRKXvCGPIi\n4olInWzQSsrUdQrR5jO0KgMynBc0dU3oZFTVmG5H0O930dka1zbrpAZo2diiyBcr/nyoaL8/4Nre\nMOlndco1VsqkvGFpWjefQmYZkKYB+FaPaW1G09RkWUw3IJKgND4IfJAEr5BorIsps1lDVVlyERAh\nIGNExcANR49ycHWJWTXk1IljzCrFZhgihKDIS2aTcdr6lWXbZWik0gQPUpkUemIylE5zwebbwf3R\nhGVm2ixaj5RpOkmeFwTn0TJDiOSCbERaAAUR6R2FyZhOp5hcMorwdz/yYV59+nk2Ll3lqZfPsF2N\neXprm4OXLnLzjQe44fitHDi6RjY4wHg2QhuPKbu8+PJzIJLq4OrVis985ssEmVNNIx/6zrfx1HOP\nMp1p+oMeTXOB225/I0ePrbC7s87O5pTHn3iand09ut0lzp+7xMkbb2E6nnD04CEAbjp9I6dvOMbm\n1S2mteXwkRvo9ZZ45OGvsLrU45YbT1JXY3Y2BSt5j1PHjnL2pSdhBbIy5/Evn8PWDUoa+p2c0Koe\n8jzHkK4D3zTkc8emlCgh2y28QOwrlkkZ1Np8Y7Ng+YVoTRbERSZF0zSUKm93fBVlvw8kuCHTKX9C\ntROu62q2yOZOORfFwrEmZcpuqetmYVt2Tb0obl+dpjb/u3MuTU+G13TD84I4N5cURd7eQwXWWsbj\nMVmWvcbAsb94z7vjORQy766/OrVtv+Fj7tJLX6+N7nw9xzd1Ea6bhi9+8Utcu/wyjZDsWM1kWvPw\nVx7GDq/STKZUdIkxUnRL3vqO+7nnWw7wL//Vp7lyeYv/8Lk/ZttZZpOCB/7kz7np8mGEX2JS1xRL\nLQAAIABJREFUVeR5TPZYSONkFh7xNhJTJFdW0zR459EqSXYSXpZE4s4lM4WUiiwzGJMhpUeItL0V\nbTKVljVZniFFh7LI0TpHqtZivU9YPo+dJJJMH61FVWsNMdl0lVJkxuBibPMi0qDDVtyL0um1KANa\np+1iluVUtSMzOVVIOLhzDms9+Ty/Iiso8gxbz9DK4OwYKSLdbofTJ0+yvFKwvSexzuPaEe9F1i4a\nQiyCk3yMZHmeNn6ynTAdGmyIuBixzu6TLIUUvK7SzDClIIS51lQwm1bYZozp91oJkcJ6CwGiTEM1\ntUjTUZxwkGdszcYcf9O9HL03ctq+Cyclj770f/APvu/7GJSW9Ssvo3ODDwKpDVmRM5lN2d2bIKXm\n6rVtLl4ckXc7RCE5fvQwdVOhNBw6bHjxxRdwoeLZ5x7nox97O8PRBR588E/Y2Z7igsRawYsvXqDT\nGZDJfdbWECiLgsOHDhCixAbBbDZltDfkT/7oj9i8dpnTJ44AntNHTrG9tUFdeS5evMqJUzegVfq8\nD6ytoHWHK5cusqZP4FYdRqbi4LxHm+vyKiVTiPkcM1VKEIQkxpCukzwjiDTUVZuUJRIDdDtdZtOK\nLMtYWlpCqoiUEZ2bNDFDCFSZpSCotoILIciLfOGYy9umwftU8OeZH1orfFTJDNQSd/PiK2UyHiX4\nwSZceY43Nw1aqwXWPA8W2p9yNtf9znHm/cdcHpdkemloL0LsM3W02cKCRWjSHNpoGnu9SBPxzpMM\nX38DOmFJzsXLM4p8zCyMGdWKUvZ45ewmq0VJ3YzY3KsphOHll9bZWdWEsE5PRdZWjnH77UdoxIQ/\n/INnuHR+yKULV9INlmvyfECvc4haRcaTKTHUiOhwNrmAlKlp7Agllwkix3QkQThkrJE+JsdSLRDO\nwOILtFTtaHhFf5AjpKOqpii9jFY5s6qBZobJFFpHRIBuNyV2KSVRRrSh9OBQRCcoRIYRCiEk3W4a\nNd8vukxnNcqFFPepFdILtND4xlN0UmcZowChCSEio0JGj9IVMhZoYdDKEZzDNhFjpkgVcCIiuz2q\nqLBRMJrOKHuKveGIyjdMQ0OjQDYWXTtUFHgHVgq8yphUFUudDl0VyGJNIwVNM8VkJPF90eJq1Gme\nWAxkOlDToEyJaZ1rnc4aMxoQGpPHpMu2EIPGq5wGSRACCMjgiD5Q6h5NnVLhstilUF1CM0YIT9SK\nYydvJHqPoMIYhasdpSi59847+C1+i3FdceT4Cbyz3Hn7nTz2+FfwN/Y4fOw0WbHC7vhldqYjTp4+\nwYVz22xcnRGQLC2tsLU1Ym9nF6LgySef5tbbb8aULTGUeybjPR74/B+yvbvHG+5/C5eubPHkk88y\nnsL5i0P29jYRAr7nuz9AaGpOnr6HV87tcfFCQ+MrXn7lEtk1x/ve/25O3ngTRW9AppOxQhtDVe+R\nmQIR03CDejJBCY33oGTa8TStgsFoQT0b4/yMTtGjmk0J2lDkPWJwVNMhRkLwNWVZJqNT8GhdEKXE\n+4hQKcuiyP8/8t48xrf0LvP7vNvZflvtdde+ve/ttt1e29hg4zb2AMaIQRgQURQpEklGEE3+4Y+J\nEmZGbCHDJKMIosxkgpFHMzYMzAQNYAPtNtjYxsbu/Xb37b5999qrfuvZ3iV/vKeqnUjRaISRWuJI\nV7d1b+tW1alT73nf5/s8nyeWHrS25Xjt6/cM8/mc/mCFvb19JII8zWiqGucFRW5olSfsS0ajPjqx\nVAtP8PFUqY3DmD5N2zIYDCIE3tUoDSaJHyRNI7/CexFbx8XxqDKm+JSE4C3eHUsyAiMNokvBeQ9K\nZXgrcW1Xl6aIz6E0eJdQlYKiFyXKQJQztDFdQ8ffgp2wtVC1UJaBmpaqsiiZElpF3bjYExV8x5bt\njtW0SOKAKgQBviKEhs2Ne7n/wQskieD3fu/3Kcs5SsJ0WqJNyoXbztE0M/YPJGU7xiQRRlPVYK1B\niujMiMjFgOvimlKCVhFN6XyN0THpl0pNYVJGRZ9xNeVwa4aWGq0MaS/FNTXBC4II5F0ZpHUNVVuR\npglS55SHh5R1RbCxsLOuPfVizqCX0zZ1V2UTteleFjW+oijQosbaCoLtND3HvCwZ6DTWQHXadlV6\nirxHU4IQhqZtqZoGo+IADyWobcv+4R6OKQfjfRatpfWasm6o2kDrJQjDovUEYWmso2wtFknZOtpp\nTaqWEUFHa1PoUdXx6Fi1BYnMCF5RNwrfOVd8ECBjBXzrACmj9u5BI3Ed5Md64iIsHJwMekTHvQiI\noFBOIWljkAfZYRsVsjv1KCQIRWIUUkFSaPa3KnAlF198gdQoXnrpKhunNzk8OmBvbw8hJC9ffIVX\nL10kS2B5uU9iNMML57hxa8LSyjIvv3qF6XjO7s4W9wCLxYzpeJd77r6d1fVVhqtrrKyv8ZffeJ4n\nPvL9JEnGCy9+ky889UW2dm4Q6prFvGRleY2q9mR9xfLaGvuHt1g0LXkvR0qBVpHg1zYNztkOYRnT\nXBLVzaIiDlQqRWGi9qpVLOesnEMK0FoyHAxpG8disSBJopRV1xX9QQ7BY21DPy+Yz2tsGyiKLJ7C\nAOdb6qYiz6PUtCinmCSJEoWIn2fTfY6EwGw+ITVxY1FVFbkK6I7WprQE0XQ70bjQJalGtI7xuIrS\nV2IQSJomRpaPT4AhhBObWpIkFEXRnfpalITGW2azCUoDQUOwOAtGpyRGxk1CIpHC4GWCMW18saka\npboS4A6wdMz2+Oteb+pF2JiEdlZycHiA13PakEOAxWLBdDLFiQXWxgnnZDxGqAjfUJ0tpiormlDS\n7xfs7+9y66bmjrvOkiSGM6dOcfHF10mTaG/TMuBVSlUuKHoDtJpy7coW1dIKt24c0DYRkG2DI8ho\nT8n6CetZzqSqaakpkozN05tY15BRsFKkyLahUBplA7Zt0JnGZArhJUoIAo5jNqEQPu5SjMAFS1Uv\nAIVHx2ZfqbCuobUSozLqtmZexmNtb5nYCZlodrdvIalIjMB5Ry8IGttw+coNBqM+ZeVJK0eygNNn\nBrz60jXSTJBlJU0tyIXn6q0tZtMKcU5QDDK8aEhyTTAJi8bhvabWPWYkBKloWkd/kBB8oGkdjUhY\nyJxEZR2KQ+OFxqEpoxpBIxKEyKiRSC9xZGiZ0YoE6zVtkNStxwXZpSej9hg5tQHn25PWB4TvWq4F\nQni8EN9mzYrUsdh1f7xXAhliIjIEiw+exkLAMxgV5EmGraMXXocR9aJlPodHHnyY3b1Dfua//W/I\nM4VtpyQGBkXBZLrgtddvcPnqFssrSyzKlqzTaMv5gn5vwOB8xt7BLiSBU6c26PdS7r37DL1eQb+Y\n4+qrlJNdypnj5o195lOLSTXjmSXJNG99x1t53/vfx8HBHsNRTtu2ZDonBM98PidNTCcJBKQ65vtG\nF4kTAusjqCqm0iDVKVIr8l6Os7Gxoj8YMDk6wvvAaGnpRBddWhrhg6TfK/CZ6myPEmOiwwKRnxAB\nI1VRxTLQ/4/We1yHdDy8M8ZgTKAqIxf52DVxrNtaZ08kLDgeClraNrouotPiGFZ0LBkGJpMx0+mk\nkycMIZiTryUmaSUiGBoCJtGkWaQNemmRQuNQaK0wSWxLkSrCvAQKGziRfv6615t6ESbAolxweDjH\nyilW9LDWI71g/2Cfqjki6a/GGuy2ZTyed95Iz2I+p6xKrKjAW6pyyje+cY2zZz+KknDmzAa723vM\n5xWL2RGXJwcorUnVKnVVUfkFt991jquvXeV//41PMa4CTgqETHBU1NayqBZ4UkwiaW0g4FjdWGJv\nZ5vUO+68Y4TWJX4Yd6C2FdjW4toJ0sTJc79fkKTx25DkCcE3JIlAm5Q8lSyqBpMYqnbBZFrTNgsS\nAwKFTgxZniOnnWNCgTKKWVmzPEjIiiSyhZOEsrFs79asn9pA6ZQQFPOqpG4886rFC0VaJJjUkKR9\n5rPq5EFbWh3RtEckvSXG05bp9T0OxyXjOnBYOfqJRmnJ2Y1VtGowRlE5z86sYlgYnBwzqVuctMwa\nz7yNu6eZLSmB8aJl1QyZT0qGtqEVKZOyJZtV1POK0aAf5RSl6UainRYZunsXfa3xcoSIf4uLUFdr\ng4xFoHEL0+nXnV6rE81w1KfXF3zk+z5EOTtkb/sGH/7QdyNctJRdvnyT3f0p9z34Vv7JP/1fOX/m\nNFevv8Krrz7H/ffcTrBz+r0Rd99xjve853H+j3/xKVy7j+vgTINihPAN6ShDGIlKNbP5EYvFLod7\n1xgVt7G5nPJTP/6DJEJhLfR6q0wXni99+a+4tfuHPPGxDzOZHvK1v/wq88WMO++4G1vCXefvRmt1\nEoEPsXDxZKovECdugaqqutJNj2tr0B4tJYeHhwwHS1R1TTkvT9wV5WJBsjTA+7jI53mfpnExrKGy\nE61VKXnSwAHE+HPVfT6d/awoChaLOdpEyLxro1MizwuMaSgXDUpptNEEFMZEaFCep+T5cWeiQ4iW\nsqywbezDMyZha3ubNNUnwzZrLb1eQZ7nJ0zj+dySJClFT+NDQ9s6tEypqjm42Fl4fFKCY704+qyd\nsyDCG1Q5//+PYvhPvd7Ui7CQsLGxwf33nqLyh0wrSVXtkAjNnXeeA1UzXniuXdkjz3PWNpbQSjE5\nhMXcs7a6hpOCG6MGnaS8+95H8MGipWM0yPnJH/+7fOYzn2UyLhFSUFUWnbSU5ZxhXzIaFiw9dD8/\n+hM/wHOXnuU3P/1HLCrLoJegZITOz8oxtgUtYDIpmU1n1PWClXVDPqpQoqZX5NjW0+uvAAZtEpZX\nR7x8Y8a999/HuXPRxnTHnRdQQbO9M0FKxZkzm2RFj2efu87bH3sbZ073acpDMmPwDdza2iXgMVkC\nSsWdDiIuVlJTtxE/qz3s7E8YLGuSIo88glQRFNjg4+KvLGjwNhaTmqRAmxobBOPZnNniEG0E07nD\neh+9vqLjNhiBaObINiczMCxM3CW6ljYo5lM4mFpEtkRVO8ouYXk4yZEqY2u3AuXY3dplfS0jYChb\ny7XrW2gqzp5aj/qjkLgQNeRImxfdD2xcYI8RlwI4Hvl7IoAG4sk8iA7cT8ckJgZ5GttSVoELt5/j\n07/1ea6/vsWrl17mQx94H0/+yZ/T2PjxG2u5emWHf/SP/gEf/6Hv4y0P38Nrr17ki09+g/Pnz3Lh\n9jv5337jXzBfOIIXfPHJL/LO7/37vPTCy2SppijyGAQZ9kFogs1YGi4z3jvglZef48EHb8OMhiwW\nNdeu7ZAUS+zsbyFUPNVsnl7H+yGzxZThcEirPUliTjy1updyzMOORoJY8eV9JNDFxVKSpgnBAcaB\nlCeuiiRJCC4wm05PrF/HXlqlFIGA7XarWoROI5UIEZhP5gw7YNF4PKZXDCjL8mSotVgsYvhDpozH\nY4os8okXizmFjIPp6M8NNG1NWwtmszmtLdndjdo/BMrSonUDHQHQe8vZs6fJsmgjLcvyJAH37ZVI\nbdsQ66MCvvUYrQkhDuQjE1nifHTfxAck3kchYmFCHBxKtEoJvkG8WaDuf5NX8IGiX7C+vk7pFEVr\neO21CdV0welTp7HM0bNoyh/0+5w5s0Hb1BizS69QZHlGUAXeWeo6MBoO8b4iSxJ2t7e44/ztfOB9\n7+ILX3iS97z3YRZVxcWXD7ln7Q5uO79MGjJuXL5Fr69ZXR/iqQlBI0QcpNlWEyysjJZ4/D1rNG1g\nNi8Z9JdZO7XBe7/7A7z0wjcwBhKTYa1meWWT169dIyxarIDh8hLnzp8DYi36/vYBb3vru9k/nOIB\nrVOef2mXRx59C9PxDc6euYtyOmM+rRAyWvZWVtfZGVc0For+Mo+9492E0JIliiTRrJ9e5z0+sLq+\nxmhpmZde/mf8yI/9CG99+51R52qHGG2ZVzdpykDV9HnX9X1+93c+y1sfeydr6wVts8GtnRuk3qJU\nQ9uCkp5RP0G0NWc2Vzi3OmI232Wtn+BDRWY8Wjl8CyZJcFJibYNK47DKGEPrYrdakqQ4oDdcYjLb\nYz6p6BWC6aSmblvq2RyTJPgAuvvdhUAQAqE1uBbP8VEblBC0LsL3tUnjMfz4BRUcInSdaEiqpo7t\nEQpMkmGSjPm8IjErPPDAA3zxC1/k+utb3HXPHdx24Sw+lOwf7LGxuUKgpdfPuHnzkKXRgO2tXS5d\nusWZM6d57fJ1dnf2+O/+MfzWp/4d73/fY3z+T/6ctg3ccdcpyrLi1s0Jn/o/P8v4YJf5ZMbHPvYo\nb33rgxxN5/xfn/6/USZjaX2TeRn4/Oc/z/UbV/i+j3wXDz74AM5arly9yrmNs/jgcd5SpEO0VjR1\nSeh4uYLuPmhNbS29onMUZRmlnSMEJzvWxGicjrvZJElP4r1aG5I0Wg+LQiEwBK+6gVc8BY5Gw5Nh\nVb/XP3HzHDsY4k54hneeLCsoy0XncmhAehZzz2JRcv16hUkc0bsE/X4vDlrThO3tbc6cWadtI0XN\ntsdeXoHtSmida4GoYSuV4pzF2rhoSnlsf3O4cNxAHXf91kYbpNYG24ouFi1BeJSMrimloidfigiA\n/05cb+pFuKxKtmZHXL1qCEnJrJJMJhPKyYKtrS2qdkxLSllW7O/vM1yWsRWhbWkaS13VtGIOQlLO\nF8xnM+pmxtF+xel1R1OWfP/HPsyXvvg5/quf/s/Z2bvJv/zN/8Btd53jEz/0ffzx7/8phzs7nDu/\ngcsapArcurELdT+GJhpFohLKaY2QGulBeUk5bbh6dc6fPfUyZVnRK1IkLdNZSa+vOZjU9CaGw7Hl\n+Rcusrd3mU98Ei5evMjW9W12to+oG49JUpI8Zz6b8MJLrzAdX2fYUxRphvSGvBjQNA0PvuVt3PuW\nd/JLv/rrfOC7P8jpjXX2dm6xWEyZLybM5lMa37K4scXe0ZhpOeOlS68yXFFMZ0dIv0QIFVW7hQwp\nsMyp03cwnraYZETRW2H9jjt55+MfZHPjNJNxw3PPvcazLzzPXec3+eqTT/K9H/weyukWStdcuPMc\nDQmr62dZXj/Lwe4OS6fP8i//1Wd522OPsrkWd/4PPLBMawX3iVN4HGurj+DahOBqLtx+ikRb1Nkl\ndGLora0hE4OQOh5JiX7sQX+Jcl6RZF06K0SkYts0+BCbKqbTpvO9apq2RXhPL+1R1XF3WNWOedlQ\n5Jok6fHY29/Dg/c9wFsffhCpNT/24z/OF7/4Vd77vu9hXnlMApcuLfj13/jn/PAPfwwjDT/zM/8F\n00nNp//VZ1keLXPp0nVaK1Em7gyVzjh32z3MJl9CasGN62P6gxF1NWG2EDR1wpmzZ7h1a8rR+Guk\nRUGWZ8wq2Ng4S/utPX7wBz9OXY45Othm++ZN7nnwYS7cfp62jezbo8N9+qtLWNuQa4WrK8rFAucl\nQh83aTTM53OUUhweHmKK+KLePzhkc+N0l+icnfhv5/M5RS/De8tsVjEYDU90Wylij6PS8cVW1U2X\n2OOE1OZ9/HmUomE8HjOfL1BasrdX0VQNzuZonZKmirax9Ps9zp5fRZuGw/2IGoiNPIH5YhqbNnBd\nFZNi0sxIkuMuQn8iRbRt3JzBt8HaRYMPtuN6R5tZLCqwOKcIWJT2NJVDyax7gTTUdYlJIuw/euuT\nDpj/JuIJ/01dSikSmdLr9XAKZJIyGExwVWSLmnxAS0aaJiRpQp4XtE1Dr1dQlXOSNEGJjJXlNbSa\nsrlxiqqc0O8n4AUroyGTwwPyTFItjqgWh2gVKOcTxkd7GBP5q3GIEX999atfZ6VQ+BB47bXtaF1x\nIHUaB4Ihak/zSeDJG1sE5mSZIDiJ991gyjYIHZjOHV/72tOcPhV3hl//xgsI69m6OaZp4s7GCUXd\nwpe+9GXqcsKgp+hlknLqKEuBSQzPXnyFxb/+A27sTPn5f/w/0Sxaiixgm5Y0EzQueqJ9iPXps6nl\nj/7oKzz5xa+QJg7h+wjZkKSeuoo9fWUtqaspv/d7f8ja2iplPaHoxeGhUgnGFPQGfbIkoXGBp77y\ndfI0IELJrfEEpMR6w/bOIVplTGrFymgNFQyZjEffpXyIEJokHzJZHNA7tcZ06rn86mU2N1cYDgz9\nQtCWM3ppTllW3Ly1Q69YisWkMmUyXbB+6izzcsL+3j5nTt9G3dYsL69yeBQbnNfW1lA6xSRvdPx5\nC2lagFRk2YBmy7MoHTdvHnLjxgFFonjp4lVmk0O+8pUvMZs3PHdxi9m8xfuUjVN9Xnn5iN/+zOcp\n52N86zCmx9GkpjcYEERBEC1H4wiAqeqa3/qtz9LvLZFkBhccDz/0GPt7f0FerLOytE6ROoS22OB4\n/luXuHDXvexPSi6+9DraeK5ducm1yxd59eVnOXtuk2tbOzz6yNuxriXPUxCQ5UkMFdhYZhs5wDFB\nKWVslzn25y6NRjShQknJcDjkuA0jS1MOZjGAk6ZpNwg7TqXRcYdjlRbEZ8J5j3Ut3sc/iyEMcxJR\ndi50w7Xo4AnCMez3qMuMXi8hzRxVWXKM52yahqpqOp9ufVKhZEzXmN40CKFPhndSipgGlKJLAsZ5\nRgygOHw4bovumqGV6HTdKNeIrkNSSon08e8iZ8JE2Ut3zojQlQr78LfDJ5ymKYUMEfCTGOaNPpk2\nJ8agU0krYr9dYhIGgwGL2SymdNrYPlu5mHSxresWjxyjNEYb6qrm6pXXYz9biGWViVZ462iqBttY\nykWDd5GVG7zko098iHsurBGC4Bt/9VdcvXoDrRPqqiHPengBF1+8xPJonUfecgcww7YLWtsidBF1\nWumZ1wtefHmbjVPnuO++UwD8Zz/1SZYHK9gG6NjBi8byW7/9J3zyx3+UR99yJ6n07O/ssLc9YbFo\nmM3nFP0Ula/wT3/9N/nRH/1RFtMpd164QF3NWZRTynqGE5a8KDg8KHnyT7/JxtoqH/0776Uq59y6\nMefMmTVubb/Iq5eu8vBDb6M3MPy7f/t5Hn/vB0nSHGSL1A3TyS51vUBrRdbv0dSeRkhGpy5gtGdt\npaDXV5SLGdevbbN/uEViPL51BN9iZMA1cwB2b9xkvmhpHKR9iQspw2EfbxeUizFaJcymM44O9sF6\nLtx2O1/4whepG9jZmnDt2g6f/e3fJSsEV69eYnVtjXJhydL48dN8lcUiMJ5M+cVf/BW0sbT1HCMV\nGk0QCVonlGVDfzCkqT2/8Au/RjOfsr6yxORozNIgwwcIIuGVV7c5deYMk0kEG3lvuXZ1Hh0aNpAX\nHqn6rK6dp24z9g8OKHrxBbt56hxJknCwNevYzIFvfPMZxtOSy1euQWgY9SVte4RWgo1Tm7iQYW1L\nEAmLecNX/+Lr0E657fQ5cC1LwyWWRksk2uA6LfTb639i2rOL33dRXWc7CI73SCNPHCJZliKIEPMs\ny7oBV1yE43FcxVg80OsVSJHQtp1eKiVaKPIsi04JYhehD5KayJFeWVkiyTPquooUsyzHNtUJLCeC\nf6Kt7NjpAGCOCz2D7dpm/MmLQEpo2gofPG3XRPNGNVOgaWrctxWN+uCheyE4ZwleYm1saA/dQg0a\nIVQMYrWOvEiJwzkbN2IyQcsM7+zJ1/rXvd7Ui3AgMC9LxvMZg0TiY74t9o45j/CONNUYqZhOZ4zH\n43j88ZbWxpvWLFqECCzmFfNpifd198aPgv3Kygpl6UhNDkFQNS2qMbRVg1CaedNyOJ6gvMAYjZQg\nfIsLDkGNlE2XLsoZDHJm8wX4GmNKysUWTb3PcJQCDp3E9tjWemaTA6QI7O7usr4Sj3CvvPQSSkgG\nvRFGZ+S9Ho+//4N87qlnkQiee/pZhG9wdYuWCbNZSVmWGAMiaekXOdPxmFMbaywWE4o8YX9/itbQ\nywuyomA2bsjTnDzLKfICLQVrKzn9ouD22y7Q1oFzZ84xWexRVRWnTm2SmBSdAKIkbIyoywnWtSyv\nr5GkQ/78qb/kkbc9RpEbjG7xfobwDe9+57t56MEH+dKXvsCN3X1u/e4lPvzBR7nj/BoAH/+hd1BW\nNVoVmF4fkw2oG8PVK9f55Cd/BCVbnF1w4+prfOvr3+SO287xsR/6BLbVrCyf4id/8qf46Z/+aZZX\ncj71qX/O2x59O29727t46qk/5tTpDa5d32e0fIZ/8mu/xg//8Cd45zseYXmY461FiQRpDHXtAcWl\nV17jf/jvf56PPvEBrr1+hVuvv8a5zTWWhzltVZIUfep5SzktWV1aJ00Mq0srrC4XNNWMzAQODw64\ntTvhxeeOKNtIslNdeuGFiy9TziBToA0smqhpt20MG7RNxdG+p5zD0gAuX7qKl1fJliUOgUkU33r6\neVYHmu2rNWvrA3bLv+C5py+yuXKGsoLnnnuVlTO3Y+2L9BJBTwr2Dyc4meOSDC8lTeNwHiySnVs3\nWVkdIn1ga/uAM2fOUZY1ZVnjgqJuA/OyxhiB0RIlow97saiRwmN0AUJibeji98kb/PPQJUGFQEhN\nkK7rgYw2r2G/x/5+A6IlyzUBT13VOCdRWqDRXV2SJ01NRzRsMSahqmpMohHEuqLYlhEdGK11IBR5\npsk6acThO0dNTP5JKZBBEZBIFdAElJbxYxtDvbBdcjOSYaxruwFloG1q5o0DLOt+8B1Z597Ui7Dz\nNVaCGQxY+DEBgdKeJDUIbXChxZXjWPetUwajFRbVhLQwmEwThCTP+iTpgtZJtBnFOLIJtBZaH5iV\nB+hEY11GEIGQKdqqJvWasrHMledwdkRPCqRvqaYzjFqmLscMB5rF4ogQCr7rXe/jnnsfZHvnFlkx\nZHxU8/j7P4hWJc6N0SZw5doNHr/nIaYzECLjM//285w5e4H3v+9RAB59y0PYesbmxgYhCHb2xvzp\nn/wBW9cvc+XSWYpU4m2J9BajYhdW61pmTPGzKaE6wteHHOyMkcIRlgYkTLGLBqNGKNli/ARX7WDL\nlsOdSxgDvXSAryYIeUA/mXH55Wd44eUrpDpQL24RjMKIBHyNa0tcuUCKQLljaeSE3FXlnpKPAAAg\nAElEQVSE2QGLuUXIGqVqZPBc3t7m5quXWDq1xkN338e/t4753gFH8TDDeDZlPq+ZTaKWmfbmtC7h\nYDbj9auX2VzrUc0PmOxvo2xJKlpksLEBpK3RMtBWU3CSJ773g1y9epUXX3iayXSfZ57/GkqNOHeu\nRSrPjauv8dhb7mRv55BemtO2Fp8EgusTvMdIh7AtX/vyf+DwVotsW+5+NOX+C8tobwiZpJ213NyF\n1jk+8qG3cc89hiKpUS2o0rK9d42pdVzZc/zV80fced+9fO9H4vf2V3/pJ/CNITEZyCPm5Zwnv/Aa\nf/Lk69St4exZzbsf2eD8UsKwL1ndOM32THDpxoKLl3e4sXXA8lKffqa45/w6o2FBstSnn42QPuf6\nrTF5coZbN+YcTj2iXTDe22VrUjEvK4JOqDnCoZjVgZevXEMGy9HkRgwNETg8eBkfJC5IqjqeDHf3\np/RyQ1VW9POuQLYOZEWG0imNkxxNFnjfYJsZaffSOdjfwwtBECOK3pAgGw7G24igUUj293ZwLqV1\nuxxNS1Id49hQ0bRjZtOKspT0eoLFdMJgsIK0KatLw5jqdHU3uNYkSUaqoHIOoRMSldHWNu7efaBI\n+kxbCGJMlkmMVoiQIHSsPpvNpqhUkfg4wM3yAdPpFESMWcedb9SQpZLoVMTux+9QHcabehEW8ayD\nMoY0yfFWMOj3mfUXMetuUvCQmKiD6SQhIUVIEZuVk5Qgs5iuESBV5AmbxGBt1IJa7/AiMJ+XWOeQ\nWtGW1Qkr1BGo6oqi06TikS6a8Nu2RStDr1jmm998hn/zmX+PUJLDown93jq/+j//OpKS/kAzm9Zc\nuLDOv/70kwxGy4wnDYsWvvn0izz51B/w8Z/4h5QLy5133sWV169w8+YOJs0JIUNKjTYJaZqAViha\npA54azHB0+v3EXqAVopeL+fsmXVeevFZTm2uoESPna0JgQHDwYCdw3FXEWXIi4y2jT7cppkyGo3Y\n3z1CFQOe+PATfOYzn2F3d5v77rmzcxwYlHAEb5HC01hL5SrKqmY2n2FtjZAlSjao4PGtJ8ka9hYz\nlEwYjz3r66e5cPttAFy7uUtTO4aDFbZvHRDMDKEGKGm4dXObap5STvfwVYWShjztES1XrtMO465G\na8Wzz34LpTRnTp9nsZixtr7Kzs6C5eUh3lvOnDkdrUnOxm5BGf2eHt9hHy3BtTz+7rcTykA5uUU/\nyxkfHbB343Uefc/DnN5c46VL1xC6z9Pf/CZnTt/H8maP1165zM7lm/SHKcnyEsF6hJNsX9/hm1/7\nFj/4SXjh2YsYRgSnkXqPeb0gSwZIBKNhAX6Pmzcus6RGDIpV5tWMqhZcfOlVrm7P2D2saOoSN0qo\nTw0oegPOXhhx5233kJlVbtyacPnmZT72/R9ibXMZWx5iQs0v/8IVhBzhVIIVmv3xlOA999xzB7iG\nIlFA5IgolWC7HfDO7h5pmtHLE2y7QAmPUWBtQ2M9QSRUddSZF+WcLFM4C9Mq+qJn05bG++jtEhqQ\n5HnGTEVYTp71mUwdaaYxRnUcCoHWEiHpyGoCowOJifGNuq5Z1AtOnV6jaRdvVBwF0EYhvcc6R2JM\nlBZCQCtJ2TjaxnVWu9AFOqIEMRiOokvD+S5tGHfqIUTanJSi6458g5ToQ4QcfTsc6K9zvakXYesc\nRkUIiG8sbdshrJWitb5rx43T16puaNo2TkFlZP+WZYXHkRVZ1MyqCqgQQnYAan0Sizwaj2mpUVKx\nqBuOb3piDEYnZHkeo5y27YoxA2urG3zso/fz1a89z/bWjL29KafPnGa+OKR1Nb2iT1UJlNQ0pWN6\nmBJszvTAkSUZbRYX1P/67/2XAFSt4C+++kxk8oqcxhqybICTEpVm9EcjEuVjISSR+eBCYDAcIE0f\nk6asra2xvLzCY4+9A+8blIjw/djjJzl37hxp9hyra6ucOXuGxEiEHXDp1T2G0jAYDnnwgXfiRIbW\nOadPn+auu+6irmZIaWnbiqYskSKQD1bwIUNnf8hd9zyAp6K1M4RfUJcLFpM5QiX0V1eoS4fQips3\nD0HAez8CH/nYx/nsv/kd/uiPPkfW0+i04OCo4uCg4qmnvszKKGdva5eNkebc5jpSdrzaIPDBEoJF\nKcF0NuaJJ76X/f0Dbjt/JxunRljfYMwy1mm811y4cBalBCIoqqqM9xiPIqatpAAlYbQ0IB2mjNWc\nTBuW1ofcvPIaZdmwsbHJaLjPrAzs7GyzNHw35XzKbFZx68YR943uRoQCGRqUL1HkaBkn9K5SlPMa\nrRSD0QC7qGkXgWpW88HveYz778sJi1cZigqd5tRB8czzL7B/NEXoHmkeaJzgxtYh08NDXnhB8v4n\nHuHc6Q3yJEerBXW9T1vvE7wi0RWT/S3wFbY1kMbTomsqhG+x9YLF5JB0eQi+ZTKdsry6QXAtddkw\nOdonT3uM+msMsh6z6QHDwRAhc+rWkWVDDiYL6qbi3PnT2HaBzQVpp5Pee+9ZnBRs7zgODiYngPU0\nTciylDQLTKYlSmmWRks0Vcp8NifPC4pCoYRnqlqUDiwtj5iMK/I8pXFRC87yGDiy1jIcLuG9JUk1\nPZ2xv39Iv9/vwFcBkyhUG2hafxKzFsfQqQ516b2PMW/eQFgGYinqsUfa0zU3+ze4xd+J6029CDsf\nSBONB0TwXUZco5WO9hClUUYgTRJ3i9pQ24YkSVhdXccLQVM38caLOC31QZOkGW3lEFJ2NhPFZDxD\npDVCxIz7G+T+Ls8uDUIo6rqKGXUBuwf7PP2nXyHQx4cewSvK0pKkOUme0npA5AiR45qW1IyYj7dY\nWevT1BX50grv+K73s+hSVU0r8SGlagXWaYpkgNQ9dJJjshyd5ihpUcKidBInxMFj0owk7XfDE4XS\nhqJY5sUXniE1mtNnzjAajhj0hzidIKRidW0DrQ1aKwKaLM3J84Tl5ZUIag+e3sCwsbnJbD6n389w\nbRkpV50G54NApxkqSSmGQxZlQKsWnEU7h0osUiW0VmCDJoicIPIOSQovXHyddz3+PTz+3if43Oc/\nz5Xrt1BSMOhnSJGytnoKVy2oqwq8pq4ie8CH6Al13uK9JU1z5vMJGxtr7O7tIHVEIPpQcnRUk2WB\nwaBH3VRkRmJtV0rpW4JLiDUPDvDMZhN2jypOrfW4fvl1rk2vs7a5Sdk6vA4URU7V1OBbjg7HbK4V\nWAtrp86yfzTjS398kXkraLwhnzqk3APg5Zdex1eKNOvT2ilV1aDUiFRJLj7/NNu35txz1lCcXsM1\ngqvbu2gzABrmC4d3BpQhCMd0USKkZzAYAAHXVrRNSXAVWnmCXeDaEi0sIrSkiQQtEEYx7KUc7lvy\nRNHfWGGQGVwDvWIdqTWNE+RZxuToiKVhn15qEKFlUKQM+ik2OFrbEoRDYFHSoZXHSIMVhtR0BaLe\nxkXfNVjnyfOC8ThuBKSEg8N9pOyxqGuqukaEqN9GG11NVXuMSVGqpa6rNxowZERJRpJZHL55b1FS\noLSh8XHoZooUk2pCY2ltQEhxQuuTUiEQBAvjSYw1p2kKMnT2tQ6J2VT4Y9i8UogANgSEOAa9/y2A\nugcErY0NGFpFT6IUiqpucd53NpKA85204FzkfHbZ9KZuYtW9LyPr1/kYaUVQNy1CapwVIBTzRUWm\n4rHjGNIhZZwO264uxehYWW6dw4buDQzgBWXZYJIM66AqW7KRRKI4HI9JtAatKKuSopfx2Dse5aMf\ne4Jf//RnOTgc03SLcOslSTJgPp9TV54k0ygU4fhloBRCxYm0MSoeiyIiC5QkCLDOk2YZbbPg3nvv\np6lLvG1RMj5ITgoCguWVVYRUHYcBzl+4nardYjAaoY0mMT2a1mISg04MrbXRFuainUcIgUoN1vuu\nnQSSNMV2RnmpJMELpEyReR8fWuomoEzRaX/gvebocIr3gr/z/T/I61dv8Du/+4c0zYIs6zEdzzEm\npalqkiSnyAcIQRdRjpFSbaI/eGd3h6989au8+12Pk5mUNEu4cnWX559/jbp2zOclt26+ylsffSha\nnOoWHxyyq9hp2gYfAq+88jLjnZL00Qu8/OoOm0sjer0h3/jWa9RBMasUvUGftz32CEmRkeY9Ns+e\n40CMSXLJ3bNDdo8cO3uG3kDg2ti2PJscIEPKeHpE3ZQsjyRbtxryVHH92lVmM3jsgYco55Ltg32u\n78949qU9ZlYTwiBS6lD4RiG9ZjqzzOcB7yRaZ3g/jgzfJEUi0TphXnskoGSM8sdThEVKj5QBb1va\nqqXIE6qmjs+71NggaaoZcrlPnipsU5H0EmxbEUQE92ij2bM1WabxLqZIG2kQncUsTQ2Nj/YyrRRK\nadbXN7g+3yXNDFmxysGBY2D60VI6l91iWJDnAdfWjJuG4TAlBEe/P+DwYIyQEpNoGhc94UliOlsa\nOB/hQ0pJ2rbBWXUCqwLV7XCPd7FgbSDNC5qmwjqLbdrO8RAYDkccHjREyatr4bAuBjlc3KQdV0v9\nda839SKc5Tl1WdFah8kUPrRxGuscAYENHuvjG9NkGdbGam+QqMTEW9SBn4teD6kUaV5QDIYc7h4h\nhMS7mD+fzUtM4XE2/mrqNk5xB0vYNrotlpdXqK0lEPXlJE2RSlE2bYS/RAUDZKAsZ/SKgkANskYq\nR39k+PgP/wQrKyP+7Mt/Susb6kVJXccH11lQSIzOcEZ2emesnEkzgzEaoyVaiBM/peqOf9GKZEiM\noW4tRhkSbcjShKauCM5jkowkzaNFKE3J8gKtPJlZRYiGeVVhaxAiRyUFEOvbrfdkqYkAcCERMn5u\nrtPRnIuwbetjVFYpjQwem0T6XJCKopfG5JTQHD92rXW0dYP3cO3aVVZXN/jZn/1ZfukX/ymTyYS1\n5U1eu3TA2fUEKeIPn1Iaz3ENjUKrhBAck8mEu+66i7a1lGVgaXWJp576M06fvpMQHFJqptMFAslk\nMmVltMy8HhOI3XZSKRyg05T7H76Nu+87z3hyhAkDrm3tsjttWV2XiBpuu+M2PvKxj2KSA4SruOu+\nVfqDXRxzHu5JDsfwta9fY3lpxMOP3gvAnXedpqkVXjYU2RLXLu+xuTlg/2gPgWIwEAwGK/zZH/w5\nN3cDtYzylBcxFDHoF8wmU7wNaClwQTCeVFhn4u9W4nwsKXBWkOqEpvTgY+mlpSWoltlsinOWxWJO\nW80p1paYTMfs7u2xvrnJfG5piSmxvb0dskTQyzQ3b15ndXUFYSTjySGD0Sp0x3glYTIZI6Wl6Cyk\nIVik0jRNjVIZaZrSNC1t05AkBucjgH48bQAi29g5TJIQQoUQgn6/j5TRAeV9xJN6In86z3PqypJl\nKXmRY0ycz2gdmRNFkXde8kChUrRI8K4kTVOcC2ilcL496Y+UnX8YohwxGg053N99o4WjawnJ0pxQ\n1vH/+9sgR2hlWLiy8+y1HbZQsL6x2cGbI96wtZa6aiI/AEAplDGxFiYRpFmG0pLDwwOyumaxmCOk\npKwbPBKt4zcmMV17RRCEIDA6kvmrqoZeL1awtNHucsLq7nZlJjVUdclg2APhybMc29RI6cjywMrK\ngE/83e9je3uL167tMFwe0jRXMJoTncqoBCkswXt8EBgV0MphdIRQKxXRlVqKyHMVIbJoOiO+c540\nLxiNRohgY4TUO6RUOGvRRlN7jzKGpokaemsdMliUtnHnqhRJkqNMSn84IM9zRoOCNJMIUpJaoebd\nS0AJlIwLr5TdhlwnBG/j4EKKuGM3kTyltSI1mrQ7xglCDKTYgDKatimpmj2EdPzsz/49vvWNL/Oe\ndz7In/3x57rdRyz4FHSNKOENGPfVK9dZW6sZDtZQKqFc1GRZwcb6KbK0z2y64Gtf+zq3nz/L1o2b\n3H3X3SytD2nqAFqS5AXD5SV+7Kd+ipeeeZmvP/NNNs+eZX+74r0f+jCjVcNgtMLzz95ke3dG0ILG\nObzzhCBYOX2KtOc5c88FpBxyc+cPuHDhAu95b1yEf+ATP0CWDJk3Y4Qv6SUrHB1m/I//8H/h5//B\n32dppcYvxohK8PWnX+TWwRQ5txzMHCIsONw/jCeU1JHqgG0Dd93zIKPlTdqFIyvAeQEiymYhCJom\nIMRx0ChBZj2GQrGzN2Y0WqLNo6yWFwXnbzuHCzAwOSbps7Ozx8rKMkvLS1SLCRunTiFwneMoR0tN\nU9cE55AiPl9JKpEdtEMq0TXXxO45ax1VVdI0FW1b4UJNUQzYP4q+XiUKnPdkaUrj5izKBUoWlOW0\n21wkKB3DIs7FlFtsd/Z0+LwusnxMyvNIqSF4vLNY3ghkHA/dlFIkXRjFh2MQfOjul4qtNd2Gwndf\nxxtVTnF3/x1Z574j/8rf0OUFDIbDuIh4ugy64OzZc/hQRW1YtGR5xmA4QuuUqmpOuLJ13ZBk8aFI\nsxRHIM1zBsMh490xVRnZoSZJYl9ciPSk47ZWH3xXpx03uEmaUjcNgeiy8EHgnEUnGb51uNBS23j8\nFDYuKkJo7r73PB/64Ps4ONxm93CLNB/QOtkxQuK/BmB0NM+jwKuAVC3QkBoVF0oJAo8koE2UO5RS\n9AYF+WBAVhR4H7DO01QLvK2xbc1sMqFcLNAqodWGsiy5tbVF1mtRyoM7xNk58+oGic7p5WfAFBwe\nHrK1s41zS/RygzGghCdJE5IkR+oiUuS0QIq4IyLEUEbwLSI4pPAIaQmig3LKWJoKkGqJVYC1mERT\nVjN6wx5NW6I0fNf7381f/sUXIkJQeZyPA1MhYpNI8PG/bevY3z+kbQOnNm9jtLSEUpIrl2/wlkce\nZ7GoaBrL5sZpDg/jIM3oLM4XiJDyqrXMq4art7YwvR533Hs/Dz/wAN7mZMUIoeZUTcvc1jjhKJuK\nNFf0+ktgezRtS+nGpHnC3sGEo2nD/b0BSR6lF6EMQaYIU5AIgzE91jfWKAYp03KHp//8y6SypTAj\n3vrYPTwYFMVwkyZkfPlrz3L15hbvfs+7WCwOWV/tM58ecdudFxDGYIWjdg2NLXHCEVREcjZtQ5AC\nbQzCpFghWZQV0kTJLipJcbFBeJI0o6wcQUCa5UitI1mwyNHSdcwkQZIogojt24PeACkEo+EIwhsN\nFMZo6m+L9SZJQp5nbN84IC/yWAPm4p8VRUE585iuSDdIQ1EUpHpIWS5IsxStIi+4cdERo4IieI82\nceAnZLQuxoGtj004ITZOO++wHpy1OBc14bqqUSqlrqoTrKYQb/TQOdeR5qTrID+h8yrXHVMiLsbf\nies/ugj/8i//Mp/73Oe4ceMGv//7v8/dd98NwOuvv87P/dzPcXR0xNLSEr/yK7/Cbbfd9h/9u/+U\nK00SBv0hddOgk3gTlUk4f/48t7ZfwXV6ZppEWtLx201JhRPx+KBVpGwZY2iamqbRlGWFkJK6btGp\nQGpNa+NuMk3TGNXUcVjVNC2JMezv79Pr9bHNPhGSHeUHHzzBW5Kk4APf/V7OnDvN9Ws7XH1tzIVz\n57j73g0eeuAcr195hUU5pWoDMo2fc/Bx36C6Y5DRColHIjodHJTqdrTEIaUWgcwkCBx5anDB413D\ndDqlaVr2Dw4iNcw3tPUc3zYRrB0kg2ESdb8sAyFIkhQpLUIZrNCothOc1XFnDcznc0JoUVi09HGA\nFRxZlrC2fpo0XyJLDcN+H2sljbN4l9C4CCjyzqFlrJCS3S7edF9vliY4qwmuZTYbM1xaYzafkOeG\nw6M95CjlPe99B3ZxSD1d4L3tKmrM/2snfP3aFT784Y9w6tRpLr54icRkPPPsMyil+cxnfieebLxk\nbXWDLCsIXvD008+gh5pnn7vKhQsXePChhymGIx56+C1Ip5iOrxMCFL0llClofctwOGBlfY2Do+sU\nowHelcyqCiNzGidRpkClBpMl6MxQt/7/Ye/NYyzNzvO+31m+9e61V/f0Oj09m2eGiyhRjBaPJEoB\nNZChLIatKNYSyEngPyIkCiAIVCREChAFThwTNgPEMQwBUYDIkhWSsjZaCyONSIrbDGfpWbp7eu+u\n9dbdvvUs+ePcqhG1U2JiAtEBCg3UrVtduPd+73fO+z7P86MxS3RV3MFYiRc5TrRMpg33711lZ3fG\nzv5tOiuKbqQYRl3OnXmU1iusTIjSES0N937lLd7/7e+h349JY8FsNibK+xjfEicaJyxRpkEYhAzJ\nYI0tw24uivEqCgVXa5SOKaoK17YMexlVXVDVC3pDTV23tLalqCtYCBZllyzRHBzssbYywjlYFAvy\n7ig4VeOEKIooFlMSDXLZHjOmxaNPhl5aa6qqZDgcYtqWNI9YzB3Ovs2+W11ZDRZpqbG2JMoivE+D\nddgHOZoiDNeEDC3BNE1DkaZFxhGMF0gR2hXSNUgF2ki0l+SdbElNlhjlkUqFLIsTSKhdMufCZXDy\nxVKxegz39WGX/XZ86l9t/blF+P3vfz/f//3fz/d8z/d8yfd/4id+gu/93u/lueee46Mf/Sg//uM/\nzs/+7M/+uY99OaubZfQ7A2xTodB4YZDCcOHiGe7tvEnbtiRpStMYjg4neLOJEpY0gd2DMavDh/Be\nkqcZkRS0VUUn26CTZ0ykZb5YkHXDB2Q2XRD5EdLMUFGfRRvhWoOfznjr1ZsMV2I6eUpZGZAesxwI\nWiTOhsSuu1eucuvODrv7eyTRgIeGK1x4+AL74wOs0zRthJASZ0OOQ5JmQRsZnK3EsUMgUCrFEgpd\nkiegIEmDTE4To4UDHHt7e0wmk+BIUuVyoFfhTEusBXEeqBVRJAOGJo3wSY5ShiiCXqeHM8FBGEmF\n8H2UikgzDTpFSUknzxj0MvxyqNPWBbPpnLKoaEyE1hVFaXmwe0ieK5I0IYkgEpK2MhjjsVis1Djp\n8cqzVAKhtF6md0Gn28ejaQ0Ui5oszvDOU5Ql3/Ds3+Qzv/9JOsMeUaRojEFqEFIgI82Zc2dBtFjT\n8tTfeAJjKp56+gkefuRJdnen/I//6B9z5txphqOIrfURF8+fDQE0CXzNO7+B1ioODqeAQ8ke3lRs\nbV1kMZuiVYKIBN70wScsKk/Wz4mSDGOGaO9JdJekIxEizCh6/YhFY5B5TJSuAiCzAb52pDKoK2wk\nWDu7xuhUj69937eCmCJ8xdGDQw4qT9bpUjUOM10wL1s2Nzco5gucKQBDlsa88sWXeOrxp4hEirce\nbwSRihFO4q3FNg7pBd45rHJYCZ1+H3VwwEq3i/QpbVvQ6+dsZCvMphVrow1aI7kl7vHQ9iYrwy5l\nMeXU9imcDbSZOO7ROodtNWHGWpLGmlg26JOsimVLQhgQIYO7LBqU8LimoCEO5UdYFBJbO+oqUDNm\ns4JF0aDiKcVsFx2tIn2CaT1RBtYuqGqPVjmTozFraxVSOGazCqxGa0NR7zPq9rBNTFmXpIOcRXtE\naOxFpGlCWTnKoqQ/6IfQ+7pCpzlGRTSmJk4aYm3BObzVRHHGompJ0yBDjIX5smvan7T+3CL8rne9\nK7yof8gecnh4yJUrV/jO7/xOAJ577jl+6qd+ivF4jPf+T31sNBp9WX+c9J7Z0ZRuT+NdcOtkWcyd\nO7dxzhLHKVVdkmUdut0eEoW1LUp42roMMzI0dTnBOc9iseD+/bsEIGDAp0RRRJZlyyEdxMLhvebN\nG/cZ792mFydcPPsInQ3FlRufCX0nvwwRF6GfO1hd4+jIcu/+PitNmMSvnN/kyWeeYDKd0rY1dRMQ\nTCFvO8T7RVGyJEAsG/yiCRy6tiVJUvI8I84zOp1OINW2LUVVUBdzqrJgOp3StJbV1TV0lBDHoail\naYKWYfjgvUJJEfhrWUojY4QI9I4kTmgJPTUvBE6naCmJlURm4felaUyWZaHF4A2RFjgbvPhxktM6\nTdN69vbHRJEjyx1JZOkkijzrIHVMKR0IFU4Oyyk2LIuoXPbdhML5iFjHCCuXqhdPGsdM5xOeetc7\nSDohrwEnQlau9DgMXrgAPVISZ1qcNCgRXmcdSVpTITWsro/w0qGjoN1GOIyEWMRLjahFq4xFO0MW\nDiVTvICmLvBkWBvTWh9iOT0Yp8nTFGclUkBrBNZFIDRxqknyBLO8xCwaEgNWkWZ96EQ4ND6SqLiD\nMwprKkYbPdrWInWM9C3dpEOapaRJh9XV9TAz8C2xUqyurC7nIhYtIyIZI71aZkAIXO0QNuzkVBRR\ne8dsMUM4i6lrTDVnuNqjqkpsU5PnKxSFQaiUNE5wtsXbimGvS1XUpEmM8xZr1VKVIsGH3aMUMVq1\nHH+UO3mXom3wBGNEFIfgmzRJGfZzrINUjzAMkcIT6ZReR+J8RaebUbcZUklW1oekUYo3Gd6LYBrS\nnl7cYzI2SKkR0hErQRbnRArQDilbWlPSVIIkDa/hbGKIdIL1MJ0tMC4k8h3rha0xyxZHzHQ+5/RD\nW8SqRSGwbURrM44WD/BSsrG+QW8w+LLq2Z+2/lI94fv377O5uXnSE5FSsrGxwYMHD3DO/amP/UlF\neDqdLo0Eby+lFNvb20EGszMmTvpYmyCEp6prPvGJT7C5NUSq0G8LeBWBW+LppRBBJ7pEqXS7fcri\nJqurK3S7HfCeurqHc462bSmrisPxhLJeRWiJjgN1IkpikjyntobUQZKlzNVxBzdgdrwPfCutA6dL\nqUCj3dzaCtil6RGd9O0QEXmsV1wmWrX2bdz2ceReloUYwyzXqDgFJNPpnEV/QVVMsXVFsiy4UoVW\ni47jkDS13F1q6ULakyfg0Ze5qccTYHlMIGgDEcH60MY5/lusdSch6VJKvFDgPDLSxHHo0ek0JfIJ\naZKQ5ynWVYFKspgxbiuUVwxHK8QrA0wT9JWxVmGHBBxTC6RcFmQvAI2QLC92jzGWSMc0TUWv18Mv\nsT3W2iWkKDw/iOjDkEzhUQKcM5illlpJEWjO1iKcQ8mgF/aSk8HvykqgqmRZRlXMSaMkTPcjFYZ9\nVcC+B9yNJJGBNh3JZKlkkWRxTlE2J8SIgBcKf2+vl2MqR9N4FsU8BOcvoxe1lF+LVuEAACAASURB\nVIhIE4kAuhQqJkm7SJ3StpZOp0On06Eqp8RxgvCOc+fO4RqwVZCCSSXfDvAB6qZGLN/P4PISb2ti\n+cM04Zq001lmrCyI0pA8N19MWFvJmc6mzI5mnD17mqPZEU3tyKN8mfEboaRifHBEt2Pppp0v+d1h\n45RRV7OQ+bA0WHgUaRIzuz8jj1PmM0Onk9G2Da1z4MMQDB+C6OvahaQ3fbRUDIUTm90LQV3KVehI\nh5+Rik4eoxzEskvZhhZkt9NBiOWGJM+ZzZvgtFUqxBks2yJVXVPWE3r5CioJmCbnE2bzBo/C4ZnX\nNZOiPamHf9S40e/3Q/DYX2T5v+B69tln/Ztvvum99/7ll1/2zz333Jc8/oEPfMC/+uqrf+Zjf9L6\n0Ic+5C9fvvwlX88+++xf9M/66/XX66/XX69/q+vZZ5/9YzXsQx/60F/4+X+pnfD29jY7OzvLnVK4\nu+7u7rK1tYX3/k997E9a3/d938d3f/d3f8n3jj3Zf/8H/xb37xyQZI5HHtugbSuKueTwYI7SlkuP\nbjGbzdi56wHJmTM9ZLSgKhyf/4M3eOyxx9jeHlDUC958Y5co0oxWNUoIPv17V/imb3ofaxsxX/j8\ny2RRwgeeexcvvvYKr71e8XVf97XEqubzn/wc3/ZtH6C7LvnsC9e59sIn+bv/4b+LFw2T2YTf+q3f\nx9OlbTWvvX4dpSO+4Rvfx95kwTNPP8liuo/GEEeCpq4A6PYG5PmAK9d2mCwqvvF9T/FD/+Af8ZGf\n+xGEl1y/dpMbN27wjd/8jQxWh3zk13+fleEqTzz6COX8iFh5et2cxWJOVbcB45IM+Bc/+3/yrd/6\nNzl3dhslHNDgbUNVFLRNS5blqHydX/xXv8Tjjz/GE49fRongFrNNSVUWISc266HyVf75P/tn/Af/\n/nczGvQQhCB17xqK46zZ7grWRfyj//nD/PAP/1cBiy5KvJ0jvaUpWhyCdNinbRX/0z/8J3zwx36M\nfjfi2ed+mF/9hR+hmC9oGouOEhwxxiR8+MP/nB/6z38I2y4QvkD4BtsavLVsbq3x6GPPoOMuP/CD\nP8Q//V/+MdYXS8q2QnmFcy3jo0Ogw5XXb/C//x+/wH/5X/9nrG/0g+RPabSIqduG+w8mWBtz994e\nH/nYL/L93/d32FzrsnPvDkoG6dKinHP69CN0upt87Jd/mboteOzxc8ymB+Rpyjuffje3b9/EOsg7\nA0Yr6/yTf/q/8a3f9k2kccQP/oMP8/GP/Bg7O3eJRErdWByKlbUtfuZn/iF//z/9fmxb0u9lvPby\ni1y69AgOydnzl3j1ypu8/MrrHB0d8e3v/xaqcoY1JSujIQ8/+ihtaZFWcvPmPT7yK7/G3/1738No\n1EPbht/+1/+aF154FRt1sWlGqzUPdg9RrWFzNMRUc9Y2R8zLCYNOTqe3yt7eAqFSZospUexYGSQ4\na/AWkiQM9oqFRkaSnQcVWd6Spg7lE4Sfgo35F//qVf6jf+8xnJL87u+/ziOX3svK1pzdOws6ccbq\nIJysbt139FaOGHb7vPXmjG43Z7AGXoK1A8aTgkG/RrSCtuqRpDGN30PqFmcz4mjECy++wKOPnaKj\nLLNFysOPvYPnP/VxhgOPcuDqHtNScHf/Pg9t5fQShXGS2bxlc+scV6/fIMtSPIZiPiHvdKl1lwf3\n77O1OsQUNXka5gFF3fD4M4/yxdc/z7kzW5xa2+aX/uVv8HM/93N/4k74L7q+rCLsl33hlZUVHnvs\nMT72sY/xXd/1XXzsYx/jiSeeOGk3/FmP/dH1Z23bkyQhy1KMmy/VDSVRFHPv3n3OX9jGWhs0i7ph\nf++A7e0M4duQ/ev9MqwjHPfbtqVtW67feBNnDVqtkKQp1hp63T6zowlN02CMoWmaoHPVmqIsqasa\nXQvWNza4sdQOWt++zd0Sgrq2CAGDQU6v3+VgVlEsqmBW8AZn7ZIMHAYlwjvSNKa27oQA0DQtX3zh\nJX7vdz/NoihpTMt3fOADxHFKvzfgmLZtraGuqyVqxYAIuBalFSxvfscymqCrYElKCP/vaLSCNWaZ\n22pCT9iDkBJB6CW3rcF7T1mWdLKEJF6iza0FBAJB05ZkeU5VlbRtg1RiaefMoDVMyxl5tx967VFM\nnsa88fqrFMUBzz73w0SRJE4Utgk9dqkCMkbKEDCkZYJta6wRtI1FS8Xe/j5rB/usredL/amhbmua\nasZrrwYyyRuvvcJw0Gdj4zxffPU6WnvKRcliJul002Ct9eHzdfbsWbxPyfMhnU6HZ555hsV0h+13\nvINiUZDlKTrWTKeGbm+Nuqm5fPkS73nPO6jKKfPplH6/z8WLF8m7fRaLmrzTxxjD2toq2fLo3+12\n2dh4ivlRiTHgpUaqhCTpcurUKZypiLXgG77h32GxKJE6Ae+Jo2jZN19m+/qW+byk2+kuQ8dj2qLF\ne8/21hZZltGadqklX/beBSitqFw40vsy5PgmSXLSntKROqEW13VNmqZMZ7sMuhHFYo43oHUXB8zn\nNXkv48yZszzYfR3rPA/u3WVjPUEtS4qUkqppMCYoWGbTGU1jWOkNKaspUkWMx3N6o+OQeEGed7B2\ngjGO1dUhb928RzeP6OcDpo2krmtUGkwVSkbgod/rEekoyDeF4Pz583z+xZQ09fjGIGVG4z2dPEjh\npDdoqdDa0+11g/ZXSmwb2jTH7QqtBZ08Y9605GlK2wjyLOby5Ye58eAKly6cYnsYIlm3t7e/nDL6\nx9afW4R/+qd/mo9//OMcHBzwAz/wA4xGIz72sY/xkz/5k/zoj/4oH/7whxkMBvzMz/zMyXP+rMe+\nnCWEoKpqhLIn/VRP8JbXdY1pDQhOYIQQ0tMWM8NsNqOuA81VijD4yfOc06cfYjIeMz90zGcFeSdF\nL+VoUggiFYTnUkgiAU1dB5+605TzOcYse84u/L/9QY+d3TlpOqSuHcPRgNlsRhQnIY3UWVKlTggc\nWoR+sBCetg5ZCMaGvuHP//wvMt4/YDqt6fVyvvjiK6xubNLtbCyF4pI0ySjnNU0TcO9JEi8HMyJY\nu5dUXfAnN83jXrDWKhSfNDl5jb0P/eE4iamqxVJX6bC4ZY9s2RPmWKoTfqfHEymFMQ1xpJc/65dW\nb8l4OsMtXXxaBwVEpDSf+fQnqZsxAC+88HkuP/wwnTyltkFFEWyjgXR7QkIgZH+E4q8pisCEw0uS\nJGc82eO3/s2/YdQfolXM+soGEsetG7c5OpjQWsfBwZg01+SdlLZ1y4yDt1+fIDsK1OGb19+km6Ws\nDFd47fUrbJ3aotfb4MaNG4zHY8pym4ODA1ZXepimYT6fkyQR48NDOt0h1lqE8HS7XU4tT4BxEjOd\nTulkfXSUgozY2T2k18uCLArNYNBFYVhb09StBRHxrne9i2tv3efChYucO3sO7xuMKcmTBAO0pj35\n/BsbnF1COPA+bCZcmBkYIYm0oKwqqrLk1Noq0jVUVcVwOESIwGEbjoZM5zW9QZduX+Pb+dL8ExLU\n8jyn31egYDqbEmmNEJbNzU2kmCHd2/MN7z29XkKWZegsYyrmdLtd5pMx3d6Q4VCj9PxkPtG2LaNR\nh/u7O5w+HWSnZVWRxx3a1lMUC7aHHSazGdZ4Lj18kZdfeYk03aacTlksWnq9LkVREGtIlWY+n9GY\n0F9umgaEXabm+ROu3ttq3/B5z+KY01ubPHr5Am25oClbVofb7E+PuHBhm+3X1hj2YtaH6V+qrv3R\n9ecW4Q9+8IN88IMf/GPfv3jxIj//8z//Jz7nz3rsy1nHOx2xTNVXyya6W2ZFKK2o65pIp9R1veRG\nhfD1Xi+4vfzywxi4Wpo877Bz/z51rbl27Sqjlcvgw8UnvSDSmrqokD4wsryxKHn8r6JpalpjsC4M\n2vq9HpNpy8HBmP4g48KFc8R5zms33uLM2TNoYrwrQ/iQVHhrEXgirbAugBin0wkAu7sHdNKEbkeT\nJBmumvPpT3+W0w8/TSfrBh1znqOFpVhMcE4QJdHJAFLpUAy9939oUBUKzfFuqnWOTp6fDE6sCSaQ\npqpC4ZNiaXYJGBopg4bySwqw97CM9GvriiwPqgUhBDrRNHVB07QkcRZuPDqirgx5nmJMTacTPrxf\n+PxnmI0PePzRpxiub1E1gra1y9dWLuV6CpwKBg3AWc94PMHaYPPWKuHNN67x8ktXePjCRVKdMxys\nMjs65H3v/XouXJzyax//TaqyYXw4odtJiboxVgr0ckcshDgpVvN5KBRvXbvKQWcfJNy6dYs4nnL/\nwYzZbMa9+/dZWctRcourb7zBqL9KWS6o6pZef4X9gyP29ycBI78kS3/2M5+lrhes9Nc4dfocV16/\ninWSspxTliWT8T53b79Fv5MCgqqxVLUhinOMCTQKYBlgHrBBiGC3PSYtr66uLbWtAu8ddR2GddZa\nlFKUiwWz2ZxBnnNwcECiPP1RJyiX+jlCWmbTMU7EzOdzWlvQSYItXXgRdsfTKUqO8B7e8cwzPP/J\nXyWOJU3T0OtEOHNs/bXheSJQO8wytWw2nTHo95nP55Sl4XSW4Y1je3sb7y3GTFlZWQnXbdswHAyx\nrSWKUrIsYz6fBw2w6HI0PmJldYWmacIwtYmYTCYkSUqWCxKhaApJJKIlpkmghaC1grop305CEzEn\nVGUEWkm6vT556oMxx9dYs8+gpxn2I3bv3WS+lWEG+V+5xsFXuWMuSZKTItw0LUIEF0tdV2EX7Fke\nu/USp93QkSG+rq7rZZjHAKU1zoaJ/2AwIMu6mMJz5qFzxHFAhA96/ZA/KhTloghKACTeOBKlwTdk\ncYr0YFpDbRqKouBwfLi843c4e+48jz1+mZt37yF12H2XdU0WQaIUwtmlJC280SujEXuT2Ulx+84P\nfDtpnDE5Kllb2+TWnRtsP/QQn3vlJiDI0oxqMaFtGpTSVFWBl26JowlH+GPEt/ChWB63Io6/lFIM\nhkMWs+lJBsR0OuHm9aucP3eGJI5gmZW6srKCUsc763AKebsQu2XKnGBjY33JL4vwvmYxL5YJbRFS\narI04/7dG6ytrSFESycM0NlYX2Exn/P6G69xEcnK+mk8cln8g8yKpadfCIl3oGUo6NYEErWSMc6G\n087zv/ciGs+7n3mC86ceIo07tNUYLTVtZZhO55h2E61DoWP52kgplrbViI2NDbAdVkcBG9TpdpjM\njuj1Nrj8qOLq9WucO3eWixcu0uslPPLII+RJl/k8HLEXRcP+wRFRpPH+bUxPv9+n01lH2ODwWl9f\nZ1G2eC9ZW1sD13CwVyzTwgzz+QypEqoqQGzPnDkbiMrG4J1BL4sySyXQcfvj+P0BaOqg0qibhsha\ndBTh7NKUFGliGbBAWgW7bhRFWFvRHXRoTEVdV/SyDrPpBG89Sg+Zz+ekSQ5KMByOMG2LJ2YymRDp\nCLMMo6qqmqqqWSxCNsRsOmU4HDAYDDjcv8bK6jrOHTGbz8h0QqIV+3sHnL7Q5frNGwyHF8izjKZt\nSWTC5GhC09acWe1Tt1NaWzPs58TRsYoinOheffXKsl0RXkelMlZHq9gji1QlEk+sYrIULly4wBde\neGm5yQjBVgjIs5Q8VcQ6WLE31wbgY27d3eOzf/C7PHzhDGtrK0TJ/w9S1FCOsp6TpGEnJ6QAYana\nkrKpMdbjrEVKQ1OXNI3FNBLngq+8KAqctbR1TdPWWJfT7w3YWFtlMt4jzjR+yaSaLxZIFyzETV0F\ncwIxZV1jnCfRCtPMaJsGD6RZSmVK5sWCpo2pCssXX/406xurOONIooS6qcNRSymqek4aiaXLKAYv\nuHv/Pp/63Bf57d+a8l/8yP/KL/7SR9EiMKzquiFOIhZ1QW/9AnjPpYvnSOKESHZo6gWzucGVjk5v\ncBJqc5wyJggeelwonM6HG1joh+uTC9e2LYeHB7z88stsbq4hpSAyDp0o+v0BxzBF0GHX6FnGPips\na1BxSq8b6BRKCqqyCsyyOKEpazq9PuPxIS+++CKdTofd3bvYNrRfFouSREdMJnfYO5jxzne/l/Wt\n86ysraEjsG1Ai4ulljrIg6A1PtjTRej1nz79EGvr6zzzzDt46Qsvs755mpaU+7tjRNxBxF2idIXR\n6oC1jbMoLWmbGrW0wQsEOEe0dPM5p1gdruCtxTtHJ+/S1DV5tkJT12RJyurKKnEEvmlJ04xOnoBQ\nDCxsnzrDb/7286ytrZJmofVz+fIjONfSTXrMioqL5y9y6+591tfXQkBPf8j21jq2LRBe8nCcYq2g\nNp5XrrwZ5iZSgA03SONahIwwJqToNdaFxEHXIrxGCofxIe4xjiOm8xnZaJ3F4jZ5FDPo5ETaY11N\n3skD+NLDcNjDOosSmvXVdZp6zGhliBARbduyurZC20Y4HL/3/O8gI4nzjtW1EZEyJDKccpJEo+uE\nLE5JM8lIDyiOFFW1oNfpMJ/PQTjyNEM4RZqlOCzT8ZgkSknSHNNWSJ8igKKeMxz0kd6zOJqTdjtU\njaEsF2gRsZjNOZpFqPE+VTGnTWNMYygKx2ijQ7mY43VNnCcsigWTaUGSpSE2YOkfsiLcwDY31qjK\nHRQZOENraqytybKEUT9nZ0dx++Zt8uQr046QX5Hf8v/WUjVGTPE0CK9xxiMjQ5w5FnWJEBmpTtB6\nSq+TUE0lmIQk0mhpaUuDQNKNJUQelKIfd+jFMa1sOJztImyNkLBYFiWcpdvRzMsZLo6po4Q2VtS2\nJss8KgIVSSpToRJF42CyMFRG451Hek8SCWaTIyIpkBi8q8nyGCc8tbFInVDUnus3HuBVjl/qnata\no2Sfto7QsgtWo3yCM/AHn/4cd27dDjsdFWgjUZJQNo668egooSoXONtibUsUK+ySGuKWJgm3zAtY\nFCUq0gglkAoODnaDqaENXD7nBcWixFp/sgu2xiCWF7W3LXiLIGTRJjqmk8bQNNiqJVEJAkGcRUSp\n562bb7G7u8e5c5doa5YZvlDODU3l8C1U05oXPvcFJvNDtk+fo7UF3s2Q1iJsQxQ1GO9xStMCR/MZ\nOo4Ax6nTm7zza55hthijUsHcR2Snn+Kpb/pbnH7ifXz9+/8OP/TD/x3f+oH/hHzlMXyygVE9oI8U\nOcJ7tDCkCmRjiNE0ixJlPXLZ+w9BMQ2urdFCUk0ryvGCyHoiUxMpR9uUxFoQaQHOnBQ3CJkGaRxR\nlQuyOEEKRV07pEooKk/dehoHVkhkFGMaRxJndLt96rolTnXQTuOQ2iN0MCTFUYfSGKyOaYVAiIJI\ngbNznG+XzkKLVgmHB3MgIcv7HE2POJpNEJFm73CKswlYSzEfozwcPJgwP6pIOxGz5ohJ1aDiHkeT\nQ4SsMVh6axoROZywWBrwaYiPBLT0KNdn0NnG2Ad4b1hMg9EokDIEQrekPiHSHXQnpaln9FREL1/h\n2q27dGJJT0Z4ZyF1OF0i6oIzo21MqUHGbK6vEpmCNE1Y3TjFYCVjY5izojpkPkVmMafPXaYbx/SS\nKOjlowSVdVgsKmIZIeoG3zSAIx/kCNnQ6QiaAjAjqiahER1mdcb9nT1SkbC1eoGsu/YVKXNf1Tth\n5UBaC9YhnTrxa8dCYculc0pFCFkHMnHZINUQ40qEVseiqpA5gA/5scKHnqiwgEVJvzQjCFojkCrB\nGkFdh+LmUNSVo5srpBG4FpwJx+KqadjafoiinGDKGGcVs1nLYKVHVdW4ZYCJs46mMXQ7GYt5g/WO\na9eusbOzj0gGKMIdVYoEbwPqxQMOH9KwqhopPP/3Jz7Bqb/9XXRyRevCoE0KR9s2qKam1+mADwE7\nTVMixJIk65dpa4Tj99H4iCgOQni3jIFsmobFfM7KaAXPcTbrYhleEuI9kceUgbArllLR2PCz1lls\n2+ARy8zVgijWPNjZ4fqNWyyqmvliQZp2iJZzQRUlCJngpUETsVjMefHFF4iTbeI4ItBxQk5y6HXK\ngGyULNEzQU0RK0FdzPj6r3sPTz9ZcrjIiXrrVCLlzv6UdLSFyvukSN66d5erV6+zNuww2F7D1CVX\nrrxKXRWkSc69B7sB5Gpqet1OOJ4Ou9y5c49+P0QY2taSpR3mR/tI0TIe79Pv9RBCEUcyZN02lth5\nqMJx3BUVLk2IdBwwWiLGGkuiE37pF3+Jz372U5w7u8ojD5/l1OY5Tp8+T38g0ElGVbbkaZ9IZbTW\n0VYVUlp6WRpulAQVy51793j3e4Il2/pgQjHW03iDTiMSEYWUMe8Y9HIibVCiJU0VHoMVHh8JKuOY\nLgxJNwTceOPwpsXR4muBjSVCpOzvzHE2oVhU2KbGR4JmEV6jysJ4WrGoPAdHR1hfU7UJRWNoxwWV\nDdnWVd3go4hqMqF2lqZtmVWWKooQUrBYlKjugNpZZByD9BxN5xg/oPUSQxgkNwYmbcverVt0vKc1\nAp3mFPMKnfdojSfLIqzzGKnRacLhdIFFIoWnsY6yrXGzkr+xeZbXX75NRwtse0TDjFpE9DpnmNw7\nwBWWw6lnv2i+InXuq7oIC6/wzZLk6kMBxHiyKMc1NtiUvUcojXWEi1ZHmCrsDBtj8MteptJBqSAE\njFaGxLEminTgtglHXbcYp2lbgbeKPOtSFzXKSJpZjcscVB5TWmzjibIYLRuG/RHDruSwaoniDmXj\n6PiAzA5IFIXAoJViMpnQ7axQ1w3PP/88VZEQiZD0BOCtAI53XXLZx5VkccTENhwd7vHKSy/wjmee\nQGlPrCRaSFxTYXUYPr722usMBjl5HgZ2OAf2eEAXbjiT6ZS11UEoZAjqOlw4k8mE06eWjjIBi/kc\niQhgTRukPW1rqOuWLEmp2xYdpwgpWZQlztSkaQxKEumEulnw2c9/jqNxAUJRVC3BYBx2h0LHoCO8\nCRQVYw13795ldW0Q+trLafWxk0kIQMplqp7BtDXeNHTzhFRLlG/oJZLK5xwsKrJFycr2Q4G80UDa\n7fLkO7+W/kqP2dEezjuiRGK9Zr5o2N0fc/X6rSCFFJ62bdjde8DmqS0QmofORFR1y83bd4njL3Dh\n/Gmm4yPKxRHTgyl10SBUQpJ2aaYld6/eIl6SJm5cuQoSjNLouMOFhx/HtaEd9JnnP8V8MSe/cIqD\ne7v8xkc+SdPCw4+c5x3vfg+3r9/naHdGplN63QitcrQ0uKZEiQxMQ54mRCokmxnjET589pROqSuH\nFy1Wajp5ShyBbRf4piDupeRagl+go5Ru0mFRKKZlxcXBKdK4IschXcMg8SSDHkInTAvJ1atvsXFq\nlSyPEIlC4UJOCeC9Qicxg1GGjEq00ozbmjQbEClFOS9Y3VgjySzTugbXYGyDxaJizfjoiLVME+cx\n87oGpSmbBiMlTdsyreY0ew/QtqabO+Z1y7QqOZjucW6UsjAVKskYT6ek/S6td5R1jVWaWVkjdZ9r\nN2/T4hHOMq9qDhc1hZuzf1jx6c/epBNFmNagck+D4vIjm9y9fYN23mINbJ8/9RWpc1/VRdh5aI3D\naMDrcLd2Hh1FFGWgaLStBRlycI3zOO9DAlkUsygKrAfrWry3NG1F09YkcQhIt7bFeYNc5o8iJTrS\nlFVJrCWzozHYhrZcYAuPaBy+bVFChJ6xabl/5xbTI0fbRgghmM7mbJzaOLEnextuAscyMSkFi8WC\n8bhBRTFKWNSJxduHqEoJAUYZbL3WNCRKs7464s6tWzz52MVgTBCOSIUdrW1btFK8duUK+Ibv+I5v\noSrrtxUSS0WDd56mqpBieGJxbZsGKQTz2Szs3r1DKagWM3AW7yzWWAyepg4tnroxfPSjv8Az734v\ni7KgKEuyNKgzEAIv4PadO9y6cxuhe4TBHrTOkYplEL1QhP4OaBXTVMFGOplMTmRjYjlQdMv3VgJC\nyaX6IyhNmqLgsUsXmR6N+dxnPkubX6J/bo3z585w9fptQJAkArxDekiTjHFruHbvNnv373Ll1VfY\n399hVjT8/qc/h2kqomXfeG/vAZ1OTpJ1GI6uMR5P2R8fMTgYk6Yppi3BBpVFlvZI8z5CxSgliaOI\nPA/b/pXBKkjBzLYgU/AhbN07SxJFNCJiNp4QD3ucP7vOzdt7TMZjfv1Xfw1jLD/13/4PrIwSHr98\nlscfu8DWxpBORzIancM6gzeGw/39JbJLIJEYJ6kauH9vj5kf41SKEJbdB3OeurzNma3TJKkkzfNw\nqowVtYu4c68lihxPPXmO2f4uq0lKXEukWFCMwErN/pHkrTfmPPnwM+RdiZAFuPKErHHp0imUbLlx\na8awF9Eb9HnzxZfoPrLCaNDn4MouwkQIJFpJqnrCqc0+iopISEw9Ye3UJlFb4k1LrCyRNCgE3Uyx\nVxT4NqHXU9AW9Dpdxo0j0Z5OFpFZqHxFtxtx/+5bOFeQJIrWWmKp6PY7TA/2UTQo2SJ9QyQhzzIi\nnRIlYU5gjcfW4JTEWU1Vtmh8EEv6r0w396u7CEuBEdDiMUrgkZhWglYs2iL0CL1HeI8QkqIsQu9y\nqTucTSpCC1Uhj6VtgoDs0RHT+ZTZrMeiKCjKgtY19IddUAapLEI2SGEwzZzJ4Zxy0rJY1NRNTSQb\nwHCwe5eqjEmTLZS2TOcHCHV+6RiU4ThPYFcNh0OKRcNsVpBlglnVkPgGKcNOFF8jRIqQy12rFmAa\npHT0e12MadnbO+Da1Td57NFLoahIubwpWBbzOf1ej5s3b3D71i02N1ZPNJFLmQTO2RNtpGlavAt0\nC+88VRkcfdYaTDsPEMvZBG8a6rqkbWoW8xlxHLF/MKZZYsXrpgnxhvko0AfSjMWi5s6dOzjniKUE\nHE3bsigKkmXGrvUhHVnIcDEeZ8fOF7O34Yven+zigZPgJJbUESnDCUd6z3hvj+3NTe4cNbhywmL8\ngJtvfBEZJUz334s1FQ92bvM7v/nrfOFzn+L21WvgLM45FvMpw1Gfg8NxGNZI6HZydNxhUbTUbcn+\n+CZKR7z22lXm8wW/+4nfZXV1QCThwpmzrAxX6A48Ok5ohaO3ukaahkusDiey7QAAIABJREFUO1wh\nSiIGkcY4hbVBwVMWBaujIU9evsjaqIewDXlWYvE8dOY8r712nZ29I9ZXYuIY6mrK3VvX2X8AUWS4\n9HDL6ulTCDyHB4coFeFahfWhGDctzIqWcVlTU9DrRozyDO8tSazoLLN9MxlhrCGJYrLEM+wJUj0l\n7s8Rk7fQLQwGPahbvJpRyhhfThikC5xriVVD2vFBNwh0k4pEFiwmt1npn2VlJKkXMzZHinNnhuzc\nE2RrXZ4+t0rpNL/5/Gd5+tIZLm70OfIJh/OXefLSNpm3HCwapi9d4fzWOmcHKcIqDosbOLHgzOYa\nqe2zMDE7RxW9xLE1Suk4ybQqmLSOcnKPzVFELzfUrSBLc3rDjGs7d1ntOhQtbRFOov1IsHPzGrH3\npNISxeAUiEgg6hIzb0ljhwZi/j9KUfu3ueZ1RWEaUIqj+QypLbNqTuUstTOMJ0f0MkdTV1jbcri/\nT1Vt0tpyifWZhj5lY/AO2qalbQw2DmicYl6ytrrBvb2aeVFStxUqdsjIczh+QJoqkhSUMFhbcbC/\nx7wIvaPuMLwFaSKYjAt01lAXc5xZ0OvGtG0T7JA2QrgWhAn5yoN17t3fZzLx6MySpjDenwOQdyKS\nBHxlsNaFCXjkaJqWuiqp65Q8zXjzzTe5dOk8dd0snW6CsqqXAnlJuSh5+cWX2Py2b8ZaE1QMSgUt\nrNY0dRMMBUBZldRVFTKMnaMqSoqiZjIvUNJz9Y3XibSiKArm8zmzyXRJIvBMZgWvvPIy165e551P\nPY2pG0SkaOqG27dvsb+/TzfvgFRU3rD/4AHONCgRtLOBjh2TJBmisWR5joNl8IsMQxQ4CYPSSuKF\nQ4V0HoSEoijIkoS808Mh6XT7nNIdaio+8zu/yutX3uDe7h5feP43sG1DWc4o50dYayjLhjgKDDOQ\nHI2neByJDhQQpQ1ZktGLe3gpSYXECJC6obWCtDvg3s4hpq25++AQ6x0q0kRRxLgu+Ze//H/xyMXz\nvPcDcHtvFyVV4PEJRRQH23lZFDhrqKqcuk7od3Ji33Lm/Ba3b7/FmXPrTGb7vPudT560Ecr5hE6e\nkaUxUkqapuKYrOWtW07bBUoonPG0rQskGCWJkxwvBB5FUVviVNDpDhGlQdKwu7PHnRt79DqSTtIw\nGKRUEQwEJFFJMWl58OAO3mZkMZzeTHEyWg5xq+OUUrZGMfv9hkTOObWRkWWC1R6cPdVj1PHEsmSQ\netYHMeNFA9URp9cfZ6WraIuW2cED1nrvJsfgvEHbksvnthjKilimbN1J8VnKpTOrpNZTmIRrN2/Q\n7Skef3gLPS8R6Rr1G3dpJnd595NnyeMS4zWtH3Fw2PBAVHz7N78TbHDzoUfc2a0pSsn3fvfXE4ug\nv69cy/4h7O9C+kifS+dPIb1ndeOv5pQ7Xl/VRThJNFkSkyYJaaqpm4ZYS9IkxrSHpInGuwpnatZG\nQ+pCooRHxYo00SjhSGONI0GpCIMlSfIQKp10KKd7LBbF0gggWSwaOkpR1wahI5ywyFgzWcx49zc9\nRZbc4fkX3wCVgQz5opFK2FztoaOE93/L+3jk8QtMZpPlrjtoXJ0NO9Fjwf362hrvf//XMJ4a7u3s\n8/f+478NwNd97bvpZX12d4/Iuxn7h7usba3x4ku36Q36PHQmHPH63YTxZBoYcUtwZ5KkeOtYX1/j\noe11+v2ctmlPdsB4jxQhbHs6PoKzp2jrBlM3rK2tsZjN8Nby1vXreGC6KFACxof7tHWLtWH4c3Bw\nwNbWVrDWItl/sItwjs98+g8Y9HIuX77EmYdOcbR/iDCOLI5xQpInmoPdB+RZdtIDT49T35II29oT\nu/XOzi5N0ywbGJzkkAgREDVpfLwDdiAV0/kMJzROJqg452jngFdee4m9g0PK2uC8ZO4qkiQicg3G\ntiQSZJJgTLBpp0sbsJQeY1u8h8lkjsth1hZY74nSJGQ0C8l4MsVZQ6RTqkVNlKVMZkesdnvMm5LB\n+hovv/E6h0cHAFy7cRu8o5N1aFqLUDHT2YzBaEC300EKz427d9EC1rb6eOkYjHpcuHSe16/eYHV9\nBduW3L5xm9nkgCReY3NrnbXNTSobzEzOWLQQIVvOGGwbTljeLecMSLzUGAdJPkJFA8Yzw+vXrrN3\n5wE6AaE188KhpMI4RXdli5WRpa9ThLWIyT5rp4Y82I9wn3mJMw9fCHnRrkC49oQSs7I6Ymsjpd/r\n0et2GY26SCFYX19jYy3BO8fp7dNsbazSbUArz7mzD9EVNSax9Hspp7Y3UdUCqxIiaej1EjY7GVrE\nFIsp/W7MaJiR+oyhHFJMP8flJ05zan1AOupipWBrvMPNmwece+hpOumCqnGoaIO7t15keyPh1EZK\nXU6Jog5eDrh3/wa7d+7yvq95J66ZhGsGxaDbY/zgFme2Mx5/uI/EMVrvfkXq3Fd1EX7r1StUkyk6\nN7z6hS+Q5jpohasK3bY8uHWTSBdYWzPen1CXitdfLYkzw8239ikXnhe/8DmUMFSlZG/niDfeuEZT\nz5lO50SEghsIFzHTIwNSomTO0aQgSSWNERxOC/aPFiTdVeJ8FaFG6DTGzC3vfPpr2bk/oy4VbWt5\n9YVXuPjYIyfGCK2jZc6sDYn+xrK7t8cXv/gyTZtQ155f+5Xf5L/57+Ezn3oBLRKKsiHvZnhhuX3v\nAY3rMJ1N8L5lcviA0SijqAuefvoZ5LEKxIThnzOG+/v73L/bMBx0WFtdOcmAiKIUL90JwqWsSg4O\nDoOvvyqJpGJ8eBhaO01FL0/DsX+Zt5znOVEUMxyt0BqPKWvqoqSX5uzv7DI5ENy8fo1LD1+gqKak\ncRxcSJFEdlLa0jAcdOkPQlbGmdOn0Dqmk/eopgVV3cEmMbduv0me5xRHR3wJz1aAwpMkUdgdxTFN\na5gX4URwb3fMnVuvsLc7ZWfniN5wBWvqUKhnBWIayB5NWRDpkOfrjEMnCSoCayzeCWxryDrBMGKM\noykNjbWkKJwy6FgitUYoiVAKFWUYK9A6Z3K0wGPJOzFnz5wjW2ZuzKYzyvmCQX8IUnE02yVOM06f\nO0saxyHaMoloq4KiaUE4NrbPYFyIyMy7AzZWzjLe3yPWkm5vQJr3GK6scuv+PYQMWlsBCOfwbQNN\ng3DBqRZ68hLrBI2VvHH9Pq+89AZF3bAoGvCKKAuA26YSbKxv8cq1CfvzBfL/Ye/NYnXNzjq/3xre\n+Zv2eM4+pwZXlQ1tmm5sQ6dbTbpDF4E0iVqBjMg3cI3gIshCivqCXACS2ygiMQhE7qI4IZEAocah\nY4YGOwYMLuOJosrlqnOqzrDHb3/jO64pF+s723aAlmmsxFLzXu6z99Z39vu+z1rref7//w/JOM3Q\nToCVuDDmaiGxWcrazRDaoZKCTEuyXfjW/vHb6V95g7pPuH33bxEwdC7h8PY7mM0UrUnYO77D7HCf\ngpTLdeCp57+BER0XX3hAMd1nenibUK8Y6Yb7jyz7t0+YZp5gBZOjPaaHh5R7Uyrn0NkBxajiube/\nwP7RIZUDn0D1+DHGNTzz3DNIf4ZHo9I7rLa/x7PPnHDrzj7BAkLjxRHj1y4pioRbtyts11EWBRa4\n/5anbpa861uf5fm3H+GGmnL874BZY5YXlEJRBMFBURGCQQhYW4PoPNo7unpDmkkSCe0wUKYpUnpm\n44qLhwuqPEPJnP5iTZLkvPJnr5Foz/UQ6DZr/tXiCqfG2AE+9tGX8KJmubR8/ON/glCW7aLj/OGG\nz3z2E2SMqE3Fz/7c/4IIcyajQF9DisYNCUhIK8GdZ55FCLkjvyb4QeFstHJ673YOsIAgQYuEdtMC\ncH1Zk2dE1p0I5GVGW3eEDNabhuk4x4uE+XLNa198gxfe8Y24nSJEEhi6noUbEBIuzi94+eWXmc2m\nlEXMJ7i6mCOzA5qm4fz8HKU8F2cPMcZQ5DneutinbFvSPEWlMSs2zSvquiHLMkajwNnZOUIo9mYz\njLF0TYdCUKQp1sBrr7yKUo69/Ukk2iYK71PyrOD68oKuiw/v/TdeR0jFeLzHsO2ZL86Z3r59c4og\nfCVMQAgQMqC1om4alFScX15zfX3K51/+DH/0B58geI2WOQOCxXqNcY68HOOspet6RnlBVo0ps4y2\n7kkUZFmK1AqFYBh6xuNxJFKPU/rOUmjFpt3ifWBxvSSoQFVVpFmKzHTk7SFwxlM3a7I84e7JMU+f\n3ME2dXyWqyl3Dm6BVNgQWK1rkjSlMwPX2zV921CWGeNin22zQknF1dWKprtiND7k/puP6bYtWT4m\nz1KyNI1opTSnNzHnNk0SgvNRz+0sOIvYNQiSJEUkKciUzbZhaFtypRiMQOgZi27guJqgUsN2u8Ze\neX75wy+RV2ua3pNrheo1MyVYt4Gk2Ofxlee/+ec/C6pHaSgSKBPB7//Tn+af/3c/R91M6E3Jz/1P\nv4xOAvO15X/+3/4vytzwuVe3yN/9Q87uTVl2jnUX+O2PfYKjSvDZ189og+aLDy6YpoE3Hp2zd1Ky\nHhypd0xHM4xM6YRienxCNiRIxgxecHjyFE899zxp1yPznOGlz5JPRrzz7/4dUnECIsFwi/HePt/y\nbd/KO/7229BijQ8KqZ7iw7/1We4+e5u3v/M5UnFEcJ7WO65Wc24/dcjffdc7OZgFFAXpLjv5r3t9\nfRfh6TFa5fiQsXd4F60deZnjfclyHnj+mW8kK19gOp1QLz7OqAz8w3/wH6ATOD29Yrv4KP/0u7+X\nP/zEH7G5foBA0UpP23sEkkxntEYydAPBwrYeEFpR5ClD5xmNy10fLqCzPfouUA9b9vf3ePe3fDOv\nf+GzDPUK4wVCBbzzdK3g9NE1IUiC0EitCUoQXKRCZxlU1RjnPev1iqI4AGJRSvIxxkUZGUJhBg8i\nYbNck+iErnP4oJGMWG3g1S+cYpzjerFABYkHOhMNFVm5z/03LxFvXpGnKu5KAwxeIH3Pm69/kcvH\nD8iyhKooSLTmahX1rlleMXiHMIHZ9EnOhKVtGsqi5Gh/ikBiHSRKIYLfpZkNWNNTTsb0fcu67oGA\nMgIRLOMSsD22jtZWHWIbSEmBTD1eCZq6I1UgfZQmWt8ghAWv0UIhgCJNuLpcoKTnjz/xCf7slZc5\nPz+nqaFtGlTisV4hRIjhQdaTSg1yADNE+ZaUpElKU3c09ZI8z5ntz9AK8iKGyPdtQ9f2tLUDrdBp\nisAgvGLoerxxpCKNcaJZRlFUkSjcGTSaoTU72WHsCq23NfloinOevYMDRpMJ6+2G68WCwXnE4Oll\nQGf75FmFGDp0Hqgqw2a7oesu8bZHCUvQOQfVLUgzgtR0wxrjtoBEyICQCi8cQWuqaUUiEgYkeENQ\nisZ63E5dpHGMx2PmV2vSJCfLxjStoZAFbSuxtmUQI7RUXDuDyDI6I3CiwLmSoQsIaekFXLt4bx+e\nG4Z+g9aB69eXIAVpNeFjf/R5EuVxKuX3/+AVPvtJgfEeGxT/+y9/jHbV4XRAFRX//f/wIaTv6Wyg\ndYH/8Wd+iVIH3GCpDm5x+tJbfPL3P0OlA0rnNG3Khz70EX5VeSodzVHXXcfFRct/++M/z7iErKiQ\ncsb9hzW//bE/49U33mRcKZI0J4RTTq96qnHgjz59SpEJlFSoNOf+45pl11P7ijxkONswyWdfkzr3\ndV2EX371HuvGkeeCT/zJnyKlQynoO0fvFL/7sT9Gq5Y8y7i+XuJ94P/88G8TAGscq3XNhz70KzS9\nQbiAwGKFjqqFfiAEgUsLvDdAjxWKYAUBFy2Pa0Hb9ghgsYmZDDpV9LZhfr1luewjASA4EB6ZSIzz\nvPnWGT4IhIwvrkpSnO0Y2hZfeG4dH0XJmg4Y12B2/9/Y19wNInw88g69wZkeCQy924nzI5/tk5/8\nfARx4kkUVGUeMUd5jkxHdLXdSbkk2IDEs21WJKkmz3JGZREjPgeHFJqiGLFcbUFK7jz9DJv1GmOj\nZjjVmjLPSFVCmkQq9eOzBd7HpDTvAr210dxiHKttF+kX3jOdTFDCIPwWLQTJbnTkDWRppFJ0rkMk\nKgbAOEOhcxqh8LIlELA2pnhpFNYYvvjaK2y3K37vd3+Xuu4gaKTMKfOcoLPIOrWRD+aNRwvP8f4+\nMnjSRJElGevasHENwQuscZEOMc4RAaoyo2kN02nO7dsTpEpo2o7lamebHgyL1Ya27siLnMEMHB8f\norYaPxiOj28RfCBLoxFn0zRcL5bczcYYYxi6nhBW5FnGbDpjvljS9YFtu6TdGqaTPfb39wje0nct\nm/UWrQLW9Nw5OWZRD8zWhnVds60bPIbROI33y3tsCFg8FkeSFyghUF7SO4eUCV4rhFJk0kbQrO8Z\npRlSaBoxUOQSLRXCFSQokqBBBfoASnqc91hrwCu0HDH0LWVV0NvYh/ZhFLH2hWC7ECRZSm8HkjTF\nIFBSohiwJPjgCM7R9RmDUNjeUsiUTgi83/27sTR1Qu09IShkkdBsLG5QzFEY03F0NObsjYudw3GL\n85bj42PWq2v+8JP3YotQxjyTqpzyr3/vsyRZNG2BIkunVOWEx595jZc++zLOBAIRIOyc47nnX+Bn\nPvi/stluKQvJ3ZO7fM8/+xd/7Tr3dV2E58slddfjUPSbFqUj2ViQkuiK0/NLpOhj8E4SQ3oWq/vk\necxeEFLw1sMHVOMZKokZCHbXDkDJnbtDxN6ejdN456I0qu8HpNKkWY4xBh8gIFhtayZC8PKrX0Ba\nR54qejNEracPICUPHj2ml4qAgyBRQqOEQuuUzWbD/t4tnPNoHX33u+E/UkXtawiOrjd0XURvq1TH\nvIB+u2PrJdghHnOLTDIajVksrugag1AtTZMSkHRtj1KSLE1QaczmLauKO3du78JaolwtkppjTOGT\n5DlnB5rthlQrZpMJq+s5t4+PCd7z+PFjgofepAQZIzaFkIggUdKx3XQ4owkokND3UUIW+aQuctiA\nbrCQWEQz4L0gTXKClVhrogtQaawF09tdkdU0Tcen/+TzvPTSZ+haQ9P0mCHs3INil/UsSHRCXqXg\nHV1T43wgywqc7SNDrK6Z7h+RVyXn5xGYWpYlm03DeHLMbG8EIrYnlFIoJbBmINWKg4M9jI1ZDUJK\nzs8vkVpzenqGdy7itNoeb3ve9szd+H/tDG0XB5xN21JvNlRl7I1XoxHHx8esNg3z6wXVaIIQcHV1\njrU9iZIcHu6jpGC5nMe0tqri9PSMl156CaRgMLFvLZXeZad4nA9EYU7kuwVjcdYhUwku7DTgHpQn\nSGLPGIcdOpRKkCLq1AMOKQRBCJAxahR2mR4yxrOaIUaRVpMoPyx3C3zwntlsxmAjo1DrqB5JkoRm\nF1cK7DT08iaE6ksYek2wYceBiy5N7z3X14sY8BQEOtE4F6LuOkSdyJMI1sViwTA4lLI7xU0guICx\nBucsyj+RP/rdTIBoNGo9wSvStLjRpN97/ZzVek1VKTZDYPO1CVH7+i7CSVogdEqQCSoNBCIzyxqQ\nQeDQ0aRhLc7GG2qCR3rP4APjUQRkFrsb0vf9Tlsa4/++PF3Mh0DwgjQp4vcpKPIRw9DjRGA8msUh\nkUpxgBk6bu0f0DUbhNbYELMZEp1G956xBOfJlMJKxbATEVnrabthF6btEdIixZN8gR7ne7Ik2T1E\nmqIsYmEkLg7JThub5QkIQVUVXF1eMSkytFZ4JE1v6foBoWOSV91E/XSWam4dHdB13U363Hw+J0kS\nZrMZxhistazXK8y1ITiHYkrX7EJXNpuYopVmWOt488EFnnQXrBQjNLM8x3uFsTLy4CTkoxIRIMtz\n2mZL3UZ9ZdNY1tsl1bhCJgHrPQLHuCqomy1d24NIcN6zXNc8Pr1kuYpRkuvVmq4zWBMoyxFdZ2LI\nTqkwPqbuBTxZmlKORrihx4VAEBKVZYSmo7cdm3rDZDYmKwru3XtAVaY8enzBar1GKlBSRHisUkwn\nExCxz6+TjLbrWa02jMscKyRSy5hzAMzncyajiqbZ7QwRHB4dY7yPpyshEFJyvVgy9p79vT3Kahzj\nSEm4OL9ks11TVZFT17YtaZqyf3AIwVN3PcH1vPH6W+wd7nF+fk5VjhFCY50lhBh56b1AiMhkkyZG\nqAhLVKiIWFy8AKn0jlOnEQqs628o4E/eD+/iaSzPC4SOhhyPY+g7ilFO8D37B5EuXdc1fd+T7qCo\nQex+j/c3vMUnOchfTuF5kgJorb1Z/L7kmPzKrOIn8awgIhjWuZsM6ghQUBg7kO04cVKq+LNeYAZL\nUZZo7TG2BaITc7ttkOTEwiwJLmEwhqIYY61hXE0wdiDNUhL570CUpROS6M8KN8d7H0Lc1QWJSGLy\nkhc7oqyUCC3oncWJeMhAK6x3WO9wIYImXfBPFtHdgxQzGpwHXIh5FB663mCMpe0GdN1EbXEXF4JU\nadZNQ6Y1Wu9SuJJYMDKVsulXuKFHVQolIj2iXresNhu2tSHJc45GBVolN7bhk1sHJDpmFqsdIHFn\nQAM84/GYNE2Yz6+YTsf0Q4cIlumkZFLF0BydFKzbnm07sFxtabuOvusZVwUHB3fZ39+jrbf0fU/X\ndWw2G8bjMZeXl1H/6z1lWTKqYmyn1prlcn1TtNM0Y7OpabueIEN0HdrYYxdKcr2cM57EBUsnSaQQ\nG0+e5fTW45FkZZT2LFYtQoKQUZ1gTEeSeKoqZ3E93wWNW+ZXCx48eMzLL7+GVIq+HzDGI0WC1mCN\nhxBf4r4z9N7hhNxlZgR0WeKFZLDR6bRcbVFJVCSgJYMzGOfYO5ixXK7woiMISZZlcRjquQkBv76+\nJk0zbt0aYYae/emEPE3ZWhvVJ8HivWW9rRmGgc1mA0DbDTz17B2Wm5q8KHBG7Ba9GIbkrGEyGTOZ\nTWm3AyIE9vemlGXB9fU1xhhGWcZyueL27VtAQ28Nj88vuZhf0/YNz7/tbZyenhOMBbsDpXoIIt4b\nKRVKg9QC60U8eYWAUIKgIiHauYDHkZZFDPLfDUiFjNGsSRIQYkfh+LL8Dmt7CANXVxfAl0ACzjms\nD4hdJKp37oZeUz45ZX5Z8X1C+ngCSJUyAWRcRKSOGdjOkWUZfW8IQdD3w26YG3OnnfNIuYPrIknT\nlGGIbkxCzGDWOsH5AbeTI1rjcKYjS0fx9Kl26YEqUsmzNKfvepwPFHkZTxDia1M+v66LcAjs7MU+\nHsG9Qcr4hxx6Q6ITkAlCETMW1Y52TIrwBuMDJ7dP2CxXNzfPWntz3HHO0bYtIQT6YUAlMb84HmvC\njWnAWUffDwzDgCoKtFYMbYMWniKLuzyhBEpLJALjPMEZzNCxsYHFfM7l+QXX19fUTYsPc27fOsG5\nuFtvdxP08bigbWqee9vTrNcrqqri7PGjSMPNc9Skoioy/HTEdrsCAvsH+1irMYPF9A4bPNPJmOks\n4+DI8Morr2IkTGcT8iKL4TvEnYr3nhdeeAEpJaenpyRJcuNOazZb1us13sPt23eYz6+p2xUQi5NU\nCYiOvYMxOtGEEKMQXQiMRhOu5te0bUc/GBJZkSRQVgVJmu0+O0iVkiSa7aYjTRWDGdhsr7l9ss/j\nxw84uX2Xi4sl9+495NGjxxjrCUNs1UihODo6ZL3akmVFpJkkCXlWsL1e4KVkPB5Tty2bumZUVYyL\ngsXlFUJCkeeMZyOWizVN3ZPno7jA69jKQaRMZwdMJgVK7mzd2wadZOgk5eGjR2itGbqebdMwPTrB\nB4Gxgdsnt7meXzGYIRo0gHZwNE1Pmhf0bYMPgixN0TsqyWa1phxF5cP6es2ozNluDabvyLKM8Tj2\nkpVOuXf/AaOqZDaZUFQj2r5j5CZYJ/hXv/Gb3Ll9m3c89zx1M2AQtK4hVTm97xiso0gKXDDoJInY\nKKUJQUU0fZZgmihXhLiDFzpFJBnO2ChDy1I26y2JlgjvI2HFG/I0JhjGZ3lC08R3K89zXPBY60mz\njGEYIoxBKvI8p67rHUFZoXdgAmMiO/HJrjlKLJObtkVd1zfU6EhMzmibHqV1RHbtWiZSgnWWPCsi\nGTvR9J0hSXLMYBEqWveVUtGO3/eEYG604oOJBf2pwxOWq0uk8rRdh1YKG/4dcMw9oVdYa0hTcZPo\n5V0gSUuGPlp6vY+Ce5lIvA/0fQdEDNHj03O0iLsyZe0N8vsJ7n40HscWxWodQYLGwgAE0GlMbdOp\nYjSJYv5tb3DW79oYHuctiYwuLyGjmcDZgTRRfO7Tn8ENLdv1Ncb0sEt1i6lXPVIK7t+/x2iXci6I\neO/NZk2RZzg7RJR6koIQPHp8ynK9Is9zsjxHKomxlrpuaOqWetvTW8c3vPObMR6Ud9y9c8LV5TlZ\npnj22TsMdb2jjKibnZq1ltlsRggRydM0DVfnZ/S94eDgkG3dMl+sMMaCVAwmMsume2PGk2oXLO4x\n1pFIRdPXZEWCw6KUoChydKKp24bBWrohjiKNgzzPkGnCeDzCmKiEGIaae/feYLVquLxYcXp6zmpT\nY8xAlsRdya1btwk+MJtNcS5weHjAaBQzKjZ9x3LbxKFlCGRZymqzpR8MWRpPK74d8NIQhKQcjVmv\nG9rOYp3H49lstwQB6zqhKiR5nmOc5WDvgCRN2duXLBbXdG2Pd4GHDx8ynsxI04SzswumkzG9VGza\nBoDBw8OzC0bjCd4NyODJRYKSgnpbU40qijRltVyiVUAKz3hU4byDtqcsSy6vrghCkmQF1sP1Yknb\nt4wnE4TUXFxdMS4rrq/XvNJ/AZBIFdCJ2YVC9WgRwAu09AgfMKYBmZGolGa7xWiFsIZ2vaQoCqyL\nJg8VQmz7SfDWYIeOIklidKod0DoubImKO9n1enXTIkiFuEkUfNJ6eNJS8LsTZMzCjgiiOFiL/6Z1\nsts4KdI0o+/jy/kkDgUEUirMENsaEYsld73jASEkaZLFELBdey6EEJ9lBEol8c0LEhESRACtn8xz\nVPxdAgbTgojcRiHjYFE+sQf+Na+v6yKcKAjekmjJtl4zHmckScYID6TnAAAgAElEQVR204G3SBnQ\nWmBt5EYNQ0ApcbNaah2TxIoiixm7ScLe3h6Hh4fcu3ePADe8uqap8U+OV2lEuiRZDD/3vYsxjs6R\naY3zDp2npIlACdBq9xm8jzQOb5DB8fprr5FqQdfWpIlk/+AgHuuSOCjrm5rgA9tNtC1LKRn6nk5J\nnBnQWsW8iS7uHHxQWAdtZ0hTzdC0ZGkWheZojk8OqduOP/2zVyirEQeHhzTNhr1pxTvf8Tymrzm/\nOKPrOg4PD7+C+pAkCU3TsF6v6buO3jiOb92J9uT1lmqyRz9Y2q7Hy4SiHCHV7u/eGwbjaLuBth9o\n257RaMK2aRgXI4oiJytyVtstfdfyhEurkgREPIaaPmAdTMZTPC3379/j4mLDctnRd56iGjGdafwu\n87jrOvb2ZpyfX8QdnE5Yr5copUnThNEo4qCapkUngTTN8cBitWU0HiF0QhCCJJEsV1sG4yirETq1\nNPWWbRtDv0tZsNg2qDYOmeaLmkRJbt26RZaXnF/OkUqTKXUziBpPJrz11kPGkzFPIrv7wfH47BG3\nbztSLSlSRZKkJFrQ1FvKsqTvO+bzK7IkZW82ASHod27FJ89zXbe0naHIM+q6Jc9Trq6uETLSWjbr\nmu1ywwPrycsRXdtQJYYs0UzSDJyiLEd4KejNAKaLg+2hZSIdwcd0NikD7WWEkkoJTisc4GTY/b01\nYncijcFT0G/bmxS1tjU3w9+2bemGHmuf4IQE1pjdImC+og9sjGE2i9Kvuq5J0xylYj83tqHMrjcc\n37mYz+JuZjywK6IhKpu0jic358yuoBuE0LRte0PqgbhAiOAJITpcY76KR0hPQLJeb3bfG5CK3Vbq\na3N9XRfhVGsSJWjbjlGe44aO4AaODvY5P1uhdRJdbRsTA9WlIEmKmx7QMFiKImezWdN1kfjw5MF4\nAgF94mx7MvxK0xQpYbPpCCECG5/0ZdM0wVgIQyzGiYoEi+ADqYI0VeRFwf5eihIpfdshReB4fx9r\nDZt6A1IjVYITNX6wZFnGxVnso82v5pRljpaKi4tLjOkRSMrRjPF0ymisWa9XrFar3QseyLL4QLWd\nxdLQdD1ZXtD2PWboyVLNc8/cYTYpWcwvcCb2z7quI01TDg8PGYaBy8vL+DdPU557/nk265r1dsti\nviQISdcbXBBk5STuOIWgyKNGuHVbus7S1BZEQpKkSBkt1VlaIZVkXa/Jygq3XbNarwGQiWbbtNh+\nYFzuRbKysYz3cpJM0zaOrnNYCyOVkOYZeaVYr7coJdhsNhRFzvX1NQcH+7HnBzTG0gxud7SNrjeH\nYzqdslxvyJ1g22yQK8N0PKZtB7yTu8VConTU2kotqPuO2f4+V5eXHB8f89yzz3J1ecHFxSXOOZpt\nizGGi/mWOyeHDGYgLwratqdte6IkBK6uFoBg27SUmUarnCxLSbSMO05rqZtrvHXINLC/P8M6z3q9\n2Q2tAn0/UDcdxsTndzau8MQh7BNQpjcW1/UI7zg+OORoVvFUPkaTMjSWoXPUbYcPAhkch3kOQlAq\nTZGNkdJz782a46NpDFzaNjQ1bDYO62CI82AKFe+L6QJKQqJBJpDKOM0zpv/Se9gM5GUagaYh3ABp\nm6ZFSnHTBx4Gg/dQlhFA2vcepRomkwlt21LXDUrJm7ZZ27a7HbVgPIqE6xhDsCuSwSFlbGVEzp7F\nORiPctbrGik9wg5EQK1GiRSBjLOhsItbFTHT2juJFAlD35HlKT4I/FdS7v+tr6/rIvyf/Rffy/03\nHvBrv/brfPs//PvMr8948OAh3/md38lHP/oJRlXBu979TXzyjz/Fw8dnSKFRMq6yfsegs4NjPJ5Q\nlhES+AR6mOc5bdPc9JnqpkEqRRAxGCZNFYmOKjatQAhPupN5BTMQRJS7WOIuWWtxs6qPs4yryzlF\nljJYi1aCxWJBPwwUoxFZrul7gxZRvra3tw8QF41Mstk0PPPM2+h2EZHt4Ln/5kMmkxF5njGZ7mOd\nib21YbnD3UAuE8zQUJYlOkmZzy+RwnL35DZuaLG7NkAIbteyEZyenlLXNXt7+6zXaxCSq/ki9sed\noG57qtEYlQhSleK8oBhP0EpzdfaQ4Dy99QhSdKboewdCUpYzppMjEJbF8pzJbMJqtcQ6y9GtIyCC\nQtuuoUxzhIqtEKkFUg2M04osSag3a7ZtPAU4C0NwHN++hXN29/IKiqqgyIqbFsuXdlZxyGidw1uP\nNZ48ryKVWEjyfMxiUzMeTbi8vOLk6afBewbbI0TAB4t3DhcUe/vHpHnFn3zmcxHSaizr1ZqhHxiX\nBU8/fSvKwIDB9GitWK3XdLtQ9/n8Gu8dTduQZZpxmTEuUqoqQ0jFdrNB6oSqKqmqkuVyQZaVNE1L\n23Uozc3RWUoFIebqRmy9jrpbqUizjHQyxrQdk6qgKiSSgXrV0dUdYYDMKTpjwFlSAd3QMQRHUHB0\ndEAp4Lnb+6RpzPSQCLabho2Fsy4Wsu2mxVl4+jBCdhMlKEcFq92pbjYqsSGQqJTM2lgQAWc8UnhE\n8ExzRaISjAOHJUHFk2JQJEGgUmIsKALhA6kEEQSZSkE4mmFAuIB3AZt3OGOiVVuAdZDlikRqmnqL\n8NAHS5CCdKYJtscGh/chyuz0gE+iLK/dxhZS2NE/hRBIpWOCYmvpm7g5m47br0md+6qK8Pvf/34+\n8pGP8OjRI37913+dt7/97QC8+OKL5Hm+w7EL3ve+9/Ht3/7tAHz605/mx3/8x+n7nrt37/KBD3yA\n/f39v9KHSxPIRzmDt1wtFkxHU+rVK3zqpU9TG8PZG/d59Naf0jqJkyVSasRgyZQEESeyJli8F7Cj\nanhnaOpNlBLtUPFxl5vSWxBBkkpPlkuUj3SKw8M9kIq27qBtmOYpiIAdeoJQDC4wnszIshRj4+T+\nzt1DnPPU25rW9PGBzAuapmc+X2OtZzKesvFdRNEDm61lvZ6TpgrjFqRZQp5NGagRWtAOA15IpnmJ\nkooiybm+nsedSVHi8BzsH+Nd4Oz0AUUWuHXngGB75hdzLi9WVKOMLNU7LbSh7QbSvKRuO972wjto\nmxapU5K8YP7wlKwacb1aU41GuBAHlG7oCWLA9Q1aSVKdsWgMTia4JMUHx3yzwpuO2aQkz0s22xZk\nSqKha+MWousGRuOC2agiyIbZ0T5ta6mKKet1zdA5kkQR6sBm3ZHmB9jQM9IpvbGMy5JxNaLvO85P\nz6hGJVmRk+U9VWlRKsUMjlRnXNcLrB7QBLTwJImImRtBUXc95WjMtqnRSlCORySJ4vHjx4CmfnhN\npgXbdUvX97hEsl2tEUESvKTIp2RVzA6OJ6zA4cEeBwczVqu46w/OUFUjVvM1RZEzP58jgiZNFWmm\nmU3HIFrSLKPtDQeHh+Q6pZrMuFtMaLuerKhIs4qzs3OKqmKzvsL5Hi014zJFup4qlQTbsn8yokgk\ngwnUtWC5MWwbi/Ma5wOBFJFF9Y1SOZ1zLJoa1wpWTnDVeLKuodKQBcs0c6TSUuUplowvrq+5+9Qd\nqlThO0GWaVSecv/hEoB//Hfexv/9+S9yMJI8d3yLN96a87bDEcfThNevW15+vOYfPb/HVE/5+J8+\n5vjuCGc7Wjfl8aXiFhq9l9D5FOcG7jxbMi5LJFOurjcYt+Bv3x1RAMiKV5c1snW8/U5GMB4jNQ8X\njt4bnjkekZmeQQm2QYKteXpPIQko5whB0tiEyd1bvPX4LQ60IBU73JQS1B7ariZJJMpA6gJZAqX7\n2jQkvqoi/F3f9V384A/+IO9973u/4utCCD74wQ/ywgsv/Lmf+bEf+zHe//738+53v5uf//mf56d/\n+qf5qZ/6qb/Sh/vox36Hz37+NbZ1zUsvfZJJGe2kp2eP6IPi7t0Tvuc7/z6/8Tt/xBffuiJXZZQ5\nCUUIMfTdP+n3iCd0XYjWzjj57Qe3a08MmAD1tidPAlWmKBJNlqZxR1IPaCmZjErWmyXGOMoyJYRo\nje26gSTJIGhWyy3X13MODw+ZTKcQJEUxRikdyRSdYbvdIqUmTdObY3QIgbzIoiIjOFbrlmt3zWha\nUZYlxhiWy8WNREfKSKTO8ywOlFxgU2+YjveoigpoObl9i7OzU64uV0ipMYOjGhfxSDvY3TTZxRD8\nuiXPC7reMD875/DgkMlkwoO3HnJ2dnbTgyN4Ep1SZSlNXdMHh/eKwVmMc2gliI2z2OfrhIzh/KHH\neUkQO8eci5ggrTWHB7PdPYPVcsPQRxVEqhV5ntI76I1hPMm5urpmNpthHZxfXYGzNxGS+/v7rDYN\naZvEloENHB0dM52Oub6ek6QKayEvMrqmIU9z+l1fdLtZMRqPGY1GO21sVNAkOkJJjenxpqU8mDGZ\n7bFZbbBmABkn8mcXcZi1v7/PfD5nNBpx++Q2ALdun7BcLhlPxsxme1xfz3l0dhbvO4annrrDaFSx\nfnTGwcEBqJQgNFqnjCc51i2ZjKdI5iwXCXuTkr3J09SbNYoS4QesHZCA3OHtG+dwTtINiq63OC9B\nqii5kxKlNAIi6kspVCm5WGyoh8Bbj6/YXNcUgLKQJ9AMsA0gClgsYdOdYhqDtFGLYGUctgL82Wdf\npu88qkw4GBfcH3pKWTLWgUI6Mg2zskHVW4Qb2Bsl7E8TVq3i8nLOu79pny5Z8ehKcnVumNcWUzZo\n1eKsoCjgYJxzkFdoNePe8k32ZjnP3JqgvKUl5bq5QAfJNzx/SGUWkOXMm5Q3783Z3xvxzB3BOHe0\ndUJj93jl8RXPHY15+0STi8AQwKici9pw7/SKO3ePOagEY2L0azX7q20q/7LrqyrC73nPe26KxJdf\nT4wO/+/rc5/7HFmW8e53vxuA7//+7+fFF1/8Kxfhpq7Z35/S9gM4z3/1n38vn/rjP+ZqFVEvRaFR\nWtM27VcUWbcjRtx8Zh8HG2KXmRuPGRqHojWRazUMhizT5EWKDgNJqplMxlhjaZotTWcZBkeaJ1Tj\ncjfNlQxDT922dMPA9WJL1wWcN+xNE9armrPTS0Ds+lgSQewBTqezm8B3tYNBHh3v03U91g4x6zZN\nOblzByEC/WBYrVZxcOJ6sqTEWLMDdQe22w3IFEVK18UQnqOjiqbtMNaxv3+A6RzLzZysyEFIlE4p\n0wTrPFlWsN027O0f8ca9PwUhcHlOs91Gbl2S0HcDe7MJo7KKQnnvKPIR3gRM0+Fw0ZVmBsxugp5q\nxeTwiG4YmJ+eUY7HUWYIjMYTlEroBsP1Yk2R5/S9o61jgLxHcnR8wmRPMF9uWDdbECltG1su86sr\n1qsFRwcHTKeTqA01Norz5Qal4wlHJ4KDgyN0AlVVsK3XbDZrMp2RKEeiovlivliQJAlFUVJUFce3\n77Bdbxk6ixkM3gfKLKXvBjabLUPfQ5A8ePAAoT1Zplmvah68dRoNNdmC8XgCgBQJ49EU7+PCefv2\nCcvlnICjqva5vLym76MyYLNtCOGKuu4oiiLqVHVCV28xTY0wHdv5VZRv9h0uuB2ANLBcblF4vBtI\ntSZLK1x48pQEgvfotEAojU4SEBCUYAiSbrvFSwlKk1Yj/tadY6aFplCeSZnQ1+udQghefvWCZ5/d\nZ5xqcHFQboXCiniqu3NyxOWb5xGeUDcMQ6TkbHtHbz2pDgQFRgyoHAIdBEmWFEhacq1R6cBzd55m\nWLY8+0xFrgs224zTyzXNtuYLr15RhisSdYrXI9rBc5U2jDKNTyPENtcy0th9i+g9dB5lDQUFomtp\n6xoZSkIbSJo1o2pEFWoK4TGAC4brbc1x4jjODGMMqt+SSsVB8v/hTvjfdL3vfe8jhMC3fuu38qM/\n+qOMRiNOT0+5e/fuzffs7e0BsF6vI7r7y671eh17kV92KaU4OTnhu/+j7+Dqes0v/+qH6eotp4/f\nZD6/pO4GprdKur7GGovznkRH04MkPIEwxMIcABHlagTwO3pwEJIgFcZH4XoQEiliBoMMAhli6lnX\ndWzrjr63DNbjSCOZ2EYhe5bljCczLi8ucT6wvz9hPK7QypGlGd4Lrq8XeNczmYypqgKlNE3TMgwt\niIDWTzApnul0TMChpKBpG66uLhiPxwgh2N+b4p2h61raVnNycgetE1arFSAYjKHKS8xO0rO3d8B0\nNqNrE64uFig0+/uHtG1D0/cIoUikQOmUJM15+tk7nJ6egpBUZdTe5nlB3/V450iT2HtMk5QQPH3X\noXRKmkiUHKIL3AfcjmkngkTrlLozLFcbitGYLMtYr6NO+PDoiETFAVGiFcvrBevlksODGWWR0xvL\naFRG/a3ruV5dUh7MdqeAyLtzDi4v5wghKbKMq8sr6q7Fhzi07LuBLEsoq5x9Zljbc3JyxP7+BNMN\nbNc15WjEZLbPZDzi/OKKtu2o2z66uQCt5c5BmRO8ReuU2XRG13WYwUEgapw3URrpw5PnsWOzjkac\n11+/j040RVHQdT17ezMCEqUVWV6i2w4pUxaLJSMraBrLclFTFNkOy+QJzjK0Hc4OdLvE+xACfddG\n7BOQp0kMVZIFSVahkhQRBnSaEUQgEIfLzrkIUJUihsEHSLIMFwLNsOTR2ZzLywsOJgWTIuH4YEpo\nG3QwNFbS9Du3KTG4SgqJ0Dlhh/zpvabtoLOKxmcMAkI+w6QZRgqEtAzqmNaeIzILaozzms3Wk2hF\nUYxJs4pFHUkvSSJJ04S9dI97j684Pjnk6CBFdYbVMtAvOobB8kY/oIXHa1gbQVIIHl0tGUlHpj11\n2+ODwPod2cVBruQuyjUgnYGd4UaEQKIVvo+yVR0GMgFZFiM7cx03e6enpzdKoyfXZDL5c7XuL7tE\n+Iu2sn/J9eKLL/KLv/iLNz3h8/Nzbt26hTGGn/zJn6Suaz7wgQ/wkY98hF/5lV/hF37hF25+9l3v\nehcf/ehH/9wH++AHP8jP/uzPfsXX7t69y+/8zu98tR/rb66/uf7m+pvr/7frxRdf5NGjR1/xtR/+\n4R/mR37kR76qn/9r7YRv3boFRGLEe9/7Xn7oh34IgJOTk6/4UNfX1wgh/sKV4Qd+4Af4vu/7vq/4\nmtoFQ//Mv/gBLpdrfvtff5xuu+U7/9G/T6Iko70Dfuujv0+zXvFf/qf/jP/j136LRxdbfEjQwZGI\nKE8xAZCaaaGROAZjaduO8WQGUrJabwlAkqZoFRgVIR7hpKRrGtqmJUkjJlslOUhJlkbLZrRbxlSm\naJ+MwR9SSu7cuY3tW5qmvjEMRBtm2FkrPUVRUJQpSgnyPOfDH7vPf/0ffxNJouj7nsViwdHRIU3T\nkiU5Sgnm15dIGXju+Wdomo7r+ZL1uuZg/wgnNOfzFUPvkWi08rzw3AmJtnRdw+H+LZwVXC3mJGlk\nuhXVmPV6w+XVPB5Xg8BYx3q1RhA/oxkMUirarqNtOoSUFHmBFApnLd4H0ryIpwQfA1aCd7ElQIh9\ndxFjJfNU0zVr8IYvvFnzn3zHO9nbmzGbTriaz7l//02qIuP4YI/ZdEyzXXN2cUleVBzeOma+XLPc\nDOR5yenpI55++imODo9447UvIIRgOhlFV1mqKMYlF+dXNzvl6XTKU0/dRSnJZFpxdXlBKTVt3dJb\ny2pd05t4VK67jnI0jZkbg6HKc4qiREjF1dXVzTP/xHgQUwaeaNP1jSNTCMF8PufhZc3bTqakaQyj\nd86R5znL5TK6MhWkSRxuz/Zm+J0CYjIZ02xWeNvjho5RmTGuSgiBoW+JTzU7jP1ONQEkOqXIYxhT\nkgi8t3R9T9c6+s7FeNXde6aUxHnH1joG5zi9uIy6ZK1Q0iPxJCpQZdFQ0XSGLmgWm46iGKGCBesZ\nBkfvU7wuWM9PObl9Qjc0WKUwg6Xc7TqtkAgNUnn+w+/4J8ySFf/yo5/gG59+nm975zdyerHkd3//\n43zXv/cC2f4BX7h/zltvfYG/9/e+gVTlnJ93vPzFB9x+ekKRBpZnlxxMjnntaou1HuEEQqTYkKB0\nRt8NBDMwKjVS1fTWYoacJPHkaU2lodIJQk5YdgZVCqbSI0xHsB6hFNetofcKrVKmhUKbFiUFJ88+\nx6/+3qt86EMf+gt3wl/t9W9dhJ9o70ajmAPw4Q9/mHe+850AfPM3fzN93/OpT32K97znPfzSL/0S\n3/M93/MX/p5/07Y9Rs9J8iSlJxZ3iUelBXmeUa++ZDlUSqFEQhii+FqI6GS+aT/cHNiib02GgMYQ\nQiBBoAUMvUUC3sXiengY+4yD9bRDTzcYBIKua0mTeCQNAZyL7qBos/dsthuqLN2RKBKkFEB01z0h\nLkupop5xZ48G2G5r8jwj2VlZt9uaqqoQXjAeVUynJd73FHlCmedIoGsGzk/P0OUI68BYhx0GskRy\ntVhQFoKnnzphXO7x+hfvk+dROtT1A8VIstrUbJouaml3SWNZOcJ7x+A8QSVkRYVXKagMYyx1b5HC\nR9eekAw+0PZRGpaqmFPQDT15nrFYrtDlmCJLEMGR7ijEAML1+KElS2Z0Xcv+4T7BWuqmZm82oq5r\nTk5uYUzA7sLj2yYWYa1Turbf6ZtjgL4QCq0DeVHQNDGUpe9byqLEO2ialrLM0CpmYMgQic7Hx8fo\nZMWjx2eRNI2n3Sx2hTWgFFycn5GmOePRhLIsWS5X1H3UrTrnmE4jvdp4F6fuKi5S49kUgLSIpgMt\n0psAqfE0tumkElxeXJFmKZdXS9p+IEtT5ldLxlVBVRZkWcFyuaAbPLPZGHSBSjRD29EZQ/AgRCBN\nUuzg8URLrvEBIR3IBIdHRL0leudK6/uoZU605mI+R+AjHJbIK3xifrIqJUkEqm2RAWSwSCwxxdqj\nhEBLhdmFyAddgjUEXcR2kmkhLbA6h9CSii0bGu7cygil4R/8k2/h0Rfe4N75I/Ijwd13HjA5foGV\nSDlbn/Id3/2Pee3VNzByQDw4413f9m6eeWrGZ/7gEzx98o288hsfo/cK42QULIeE1CcMfpfB7VOK\n3ONosaIgKTSd7xHSU7ceIRy1FWy7NSOhmBYZVZERgmRLS48mOEHXeJSJrUpXx/f25OSvx5r7qorw\nT/zET/Cbv/n/kPcmPZtmeZnf70z39MzvFBGZkVNlZQ3ggmpsI2wWbdlsLLXtr8Cq2xu+AYg9fAA+\ng71BFhLqBqFW24ihu6uogpoSiqzKzBjf6Rnv6YxenPuNQrK9sCm1S+LZRCoU+Y7Pfc5/uK7f9cfc\n3d3x67/+62w2G37v936P3/iN33iTivvhhx/y27/92/nhEoLf+Z3f4bd+67ew1vL06VN+93d/9//1\nFyfFxB3VuTpUZMhyiNnvHnx4g6zz3iPkJOxVTHjFPGmZfGw8IOtUCnjnWFSK0miqskRIya5t8YOn\nKktKVdK3juNpSwCSFKiixLpE1SyZzxu0yZIklGS/3zGOoDV0/ZFSSS4vLqjrGqXzwX1qO5bLBecX\nZwQfaNserR8O6awKOKmWqi7YbM6nSktRmIncpeObWefhcEJrxeXlOc/tNdY6wuT/9yFilGDoey4v\nrqiqms8//xyExMVEP4z0/cD19d9RNrMs0ekHQoiU1TyL7L1nPp/Tty2D9RxPwzR7L9Eyp2QUVYm1\nIykEzhYLSmNwLidTvH59jUcQEizqilldYHCgClTIc9LNokGIyPb2mvv7G86uLsFDU2ikyp1TXTcc\nY+4GKlPifMd+f0BKifOBcOomlkDP8XBgNpsxXy2RzmNMoK4VzdmM+/sttzf31E3JfD4DJFePHlPV\nM5TSKNVmjkff05SG06mjqiqEyoD9qjR0fU9MAqUK6rrBp4AuDIf9jrv7O9SEYtRaE2MuDLqJC5JT\nrjOHIH8+TQhZQzxrlhyrjtlsRggRIYbs6CoVN3c77reCt55csTy74ng88KPnN6yWc1YLjUcSpMb7\nkCvkYEk+MhYZllQYMIVACvUGx6qlyYzc5LOjrKrYHffoFCiMpBtG5rMmB63KRBRgQ0RVK05a06M4\nmgFnZqjp+fdK4eUclzI/OfqAjBZJg9IGYXuk1PhiBtZSKcGnn/6YL7/3LlILPv38E/7s3/0FQcH5\nu0uePL0iFA1jgM3ZOad+RJsSHwa0FqyWS5azil/+pV/ks0/u0V6RnCQETZq+vxgsRnswiaJKlIXA\nDwFcoFzO0WpGwUCUBc7VpOhZLi4oSZzalm7YoVWBKWaZ/KcUSWlKkyFgQf4nzJj7zd/8TX7zN3/z\n//L3v//7v///+P98/etf5w/+4A/+v39l00tKRWEKgs8W47osiOSbO8bwRgkRU3q4g4EH8tik4BDw\npgpO2Z64qCcYe/TEsc0MgarO2t59T8dABoCUJBJC6TeweO9HhmFESZk5xjFyfr5mNmvQWjJ0PaXS\nJCL329up8oVHj86pm5qbm1ccjwe8F0SfwT8P32vf5yTk3XbP2dkZzjnefvwI5yxXjy4ZxiPPX3xG\n1/UoWRGDoq5Loo3gmKoY8CmyXp+xXC64vbnj7u6elBTClFjnGa2lG0ZUURFCQqmsBlmvzzLfIfTc\n7Q6cjieqssJUM8oJsOJswIWAQqKrChE8RVXmzz0FTZqyJMRI1dTstveouGC5nmcgTsqHz2Y1z4wG\nP6JUQumMw2vm9cR8KDN4yUc2qzWvr++YNTOkUpyfn3P9+jXjOLBezDHGUM5q5vM5+92RpAT7/ZEY\nYBw8v/zLv8L3vvcdXr58weXlBUYrfvTjTzMYp+tJwHq9pi5LhqFnNZ8hhMgQ8shE6soZgcfjkdli\ngRCK5XJJFJFg85jBOc9gx8wnsIFiypjrh4FxHJnNZpzaNoPwY+ZFJyR1Pcc6T9NkvbvWxcQnyFS9\nZy9vmC9mLNdLFvWc/XFP73ZcnZ9TmpIoekQEby2R9BNYlZIkG0nkJWaK4JKfXGIZkJVSxNmB2azC\nxNxNCSkQKWvoEYkgJKUWVCLn0dXCUguDEIkgPEYWRJ3oJ0aEISDDgA8jKYJOYTJsCFLwNIVkWZ0j\ng0F4+PmPvs5Hm6/wx//uf+dkt3ir+NM/+yu+8c2/4713NrWgIegAACAASURBVAQHr1+8pts50uCY\nacP++pr3L6/4+P4TCl+gvcEnhVAGKS06dVSlR5mAUAOlyDyKFCxqHBB6REiHDhprHX6w6ELR2w5D\noKmKTCYsBLtDT7KKUXiMBhclIv0ToKiBgRQwRYVWaqoSstU4248jpPwmz9yQQJIyw6dTwoiACA6C\nQCgQImBMIiZHe2qpSkOhFXVZInTB/aknhsRivcCNLj94BLpuwJQFWmiGsc9tnzbosgDnWc8XdH2H\n7QdikdkSfcjmkGEcWS7mbNZLyrrk9u5+smsqRueoZ2uGMTtvHIKiKRnHnqqpOfUd89mMfhyo6rw5\nFzJwcf4IvwrElA0E3ifGNFKQsEJQVWX+vmYzdvuWu5trtKpIUWLHgW7i9JbVjPv7HULKLM0rMkVq\nu2+xMcOSirJGTp774BzBeuxkfXbBZXgMkvvtlhiyucI6h584sUVhWM4bghu4P2wpiiVN+UA+Sbi+\nYxx6KiGJw8gDXNtLSVlXdF3Paew52ZFqPkf091zfXLNYzKmbGqkEq6nl32/vcd5R1zWv725oT0e+\n8MEXOOyO3Lx6TlVoztYrbl69ZrVa5QpNSNw4crGYM581XL96xZ0dWK+znGx7PKCKitl8RlFUXF9v\nGccOZRQpRiSKpphhsUghGcYh4ylJrJZlvvXJuuTZvMk2XjeigqPQJjONT5a6qrHjiJKBvhsptKEb\nLHIy8gzDiA2edrCUVYWWBSTP589espjNWC2WHI+HN6oRS0A5R9IGmRIKkfGLCbQBHyO6qjl0R/bH\nI0blXrEsSlYrTdsPpPSApAQjJaWO1PSIpKjTQBk1JIkXCVQkMJKmnBgfBkKESoGSgTiOKKkp0p4x\nZDnlbrfnz//9DXe3nn/zJ3/B+1fv0XrFEGu++Tc3/O0Pb4GS4ynx3e++4Bvf+ISq2BBSyavXPTev\nXrF9LHh9AC80SgsKEtoEBNmMVRmF0gkfA87GKYsvIdIAAZyTEDMcrFQRGQJSCZKN9H3+nT46mzM7\nZrypRmF0gkD+HD+F18/0IVyVM059pCrnaFPigs8DhZSRezak3HpIgZbQjj1KFvgJYbcqK0olkVXm\nCVMXuBHGIbfeJmqUKUjKcDi2BJdb8NGPdGOH94lE5Ox8iSoMISSWy5pZXTGrG1JIDO2QRyEx4YJn\n9PlNmIHgiqfvPM3fjFK8fHWD9x4tM1hIGc0QYL7KIOzdsWUxq1FFiZoWW0hBYQxD1zH2kdmynmAk\nmlPXgpQ0s4IoBamPtIeR4CxFs2Sz3jDalvX5Iw77LrMITge0LihMyf7QUdcNSQTK0tD1I+3JcWot\nqiyBzEmOfiTGbOFlmq4XWuND5LjLDjCjCjx5QVk3M1LK4Jb5rKZUkpBM5l70LW7K1JOFoaoM2IH1\n1RWH3tHMF/RdxzAOtMMNbz99hwtT0nvLRz/3FcbvfkxZ15zalv1hx3K54OrxY168eEZV15hC8/z5\nZ2itmNeGvt3RNCXGJDarOctZzXa75e7mhtXZhjSrOFsvWNcVOiUqKSmFYuhGvNDYIEjjQCSxudhQ\nz2b86EefcTru0arg8x9/lr/n2WxiGQict1ne2PeZxgWcjgcgIqXm8aNL/DhgjOFxvUEIwXZ7T1UU\nKAXBQUw5ystPJL3FckZvPadTT9vlTLtFlclfu/sjWk6hrEbjUyRONl3bZ4ZGpROV1ojkGcYTzXxB\n1LD3jtenllpolssZ4zCyOdtAjOwOe4zJ1mLFhIKVipQkUii0qejHxOgDiURVJpTPBYUwCW1WNE2F\ncxZLwfl5lih+8qOBzVvvcnKWz246qmrJ633LzfFjXt1eU5Ql//pPv090mVW93e24ue0wZsXdscNa\nyf/2h3/JOI4sFlv6vicS0CWURLTOBcPgBpyrCC4bU2wApwIxjcQwIGJBSjUSgyeQpIcUEEhkUSFF\nxujWszUh3iBlICpBHyMRAW+Cyf5xr5/pQ1hOeLsH2PhDZhkis1hz9pjPicbRZ1izEpRGk9xIezoR\nS02pDaO1bNYrhiQxqiCVHq0MUpmMYXQOY4o3VXbT5Jh3bQqsdQzDwHy+pJ5VODtwfXcLPqGmLXhZ\nVcjkGacUBikS77//PnVR8uzZ5yiVcYjz+RyZIn3fo/vAzc2eYoKcy5CoVIELDp00dVVRGENdGoKz\nJBGoixyVdHd/iwgeicINI8klUlDM6pr99oiWMrfmL+549eol3XHEucRyU7PZXNCeHCEELlfn7E/3\nE8S8QgmF9ZrOO7RSWXOcz2IIiaLUSCFp2w7nA00zY7QjKQWurq4otKLtTiwWM2ZNlV1ozYpZMcuR\nN37IWEDg5vaeWhdcPH6L3ib6/sBsMXuT9rxcLpnP5tzZPTFGXr58ibWe169fc3Z2xpMnT3jx4jkv\nXrygbVvmdQMIhmHko48+nED0BavVCskEciHy6NEj2vbE6xefI1zHRx98kJGSx5bdYc/2eGRVNqDV\npCRIXFysiDGxXFY8ffoWx+Np6szU9HkkRIUxmpgURmu8n5jXwOXZGU2TL4CmLFCzmqLQeOuwdmQ5\nX2BMNhQVZYN1Mat5Rpu5zKPHhxzgGRB0/UChS6rSYKSiHxzzWY2LOV6p6zpSiMjC5LQOAbowEHJY\ngC5LXt5vWcwX6KKi3+7fQNVvb2+ZL+YsSTiX9dI66gnmLvEhmz6UhMpEVvOa9njCiBNPrnJXUhk4\ntSfKdYNICTsElvOKFy9fIAk8/+xHRJ3HAJUxVFrg3IhRkeXMZIfo6Li5vaNUmropJt12QMqIdS3z\n+QJre7zP+mwlSlTeO5IIEyXNE0mYYsodnDq8pPKAMsWUQx6mYNm8KM86Ya0SMWYdcIwRIcWEwsx4\nzzeGhH/k62f6EBYAE+XsAYE3/YTRxuB9wFqXzQMxYZTCjQNGgCGhtEJJiR09SmpiEDgbMr+0MDn6\npx1QUlFVDUqrCacXGEeHdW6KStGowmRa2dihtEALBTJhvXtzEbgU8SkileLp2+8ihOCTTz7BWosx\nisUs4xVFFBRFhZaCNEZsm6EnV5tLpBTYweGGxKKpOF9vKLRDNzXt8R43jJjacLFZsd9u8aNlWRmk\nVuzaFomgLkuevvUWKWSlRPQBpTVNM+Pq0YZTO7LdH7HWs90fqKoK60eiVxyOA+0YEFWW5CUkPsSp\nNVVInUHaScjspVfZvGEKxbyp0VJgNJACXXvE20zEMkaz2WzwwSFTnhsONuBdJCaJd3l8cXNzg/Uj\nSMHg9ixXm8x+FRoXcpU2DBn5eNKaobds9zskgu1hz3K5ZLU549QPGSZOpm2N/ZC7h9OJ5QzOVkuS\nPRCHI2ObL0LnPLvDEV1WvLq+5eKtt+kHy3I5oyxLjscT3g9UtaEo1lmCdn+XkarK02xqQHB1dcXt\n7S1azd8QwipToaTk8eMr+qGn2x8pyxI7DkTvc1IFgvk87yWSkBSFRKBxNgOJCpWDY6VU01jDY52j\nqWq8dfgYKbQGoSY3ayAFRdd2lMs51uUDr27mfP7yBb331EVBcIH5aoWdyHqxb0lTqkoOP3gIQlDo\nssaGnhgDMjni2FKVkfOLChEs3mUanx32pBDoT7eMo6Upaua1IdqB2mgW8xnb44ESSK6nO9wxjgMp\n9JwOjkJ5lBAUOmFMwXJe4X0OSziOPVVZcdjd5rw6pabOYUrmSIGER8iINCIfl2JyygIIgRQCKbNs\n7iHnUcicIpLlhyCnUOBhGP5vDqcco/TTeP1MH8KQZ1JFYfKbYYKyI3NkSY6Yd5ltSsIogSo0Otvd\nMIXOAZBak1KiOw2Mg+d46Gj+AbV/sahRKuspM9hmRAg5Vdj5wGiahs3mnFPfcuqOHE5HlIR5nS2g\nIXhCypWK1gXWOV6+fEnyIS9bhKAoinzYhMxNFShQUyowgBQEJF4ouuOJu92BF6+u+fCDxywXDRQN\nKElvI34cUKpAFDKPRGKkNhrXDSiRl4+vX77E2Skw03nm8yXH44muzxfNbL7kbHNON+4JVqBMgdRg\nYmQMkZhjnYk4oksoLXE+O6WkqTAi82Bns4amqjgdDpAci8UcO474sWe1mGNDvvjsOAKJZpYDLkOU\n+Y2va4iWlB4i6/PDr6Rmu92RUFR1xaxZIoThlbrGlBVNXWFMwWa95uWr53lElAQ+BKQyxCTojh2P\nv3DFD56/wGiFd5ably9YLma88+SCQimC7emTyEqE/QFVRm4OLXqxQpc15+cXEwhfcnNzj5SS+XzG\nxeUFCI+AnMPXNJxOJ077e5azmmGwBJt/t4USSAQ316/zhW0t5+s11XpJip6b19cIEm8/uuLlzSuO\n/REpCuazGaumoj0NnNqOlCylLpFFwbEbCYLM69CGbnR0Q65c61ITrGdwPabMNvX5YkZVFjx/8QLr\nHKYoaNuOJBSd84yjnZyUoGPMUjmjMabIOwAfkaYE5fK/ERE/WGbnFf/1L32Z/c0Lnn32YwDeenzG\nZ59dU5cCN0T69sjH3/sO3ge0VrjhgMFjXaCqDcvGcEoDMgkuH605HY/c3rUEYH840fUHlMyVs9aa\nzdmSs/M1MSZ2u3u8y6CumLOoCN6+qWpdCkQR8D6rhxAP2ZIPrtqEmGSuOew3ZCVH8ChpcG66hHjI\nt5NkGsI/gWSNBwI/ZC2wDxmcrrR5E7ETUiL6vDggZh1qXWoIgmaqzFKIWO+J3mOUYrZe03YdxUSA\ncyEwWIdRgnEcJySlnpaAMJs1xBh59vw5Ssu8WFEKgmUYWq6uHhFjYn/qEZPl+rDbkVLmBHgXqesH\nyI/CKIXWBpsSsjbM5lnWE1TCp4CqNFJE3DByHHte3e852ZHVYsZ6fobAM7jAfntHoTRltcL4gGiP\nyJSoy4rSqLwkco6xH7AupyrPNzMQMcN4COwOB8pK4UOc8JgO62GMEWMUVVnmVtsUjEM/tXNZ7leU\nFfNJ8pVioO2OiOQZJYDncrNCG0MxX7Berei7FiVVxowCbT+SSDkpYhxxLmBMiTKSxWKF9Z7TsWW5\nPuN0OtHM15z6gdl8wd/93Q85Pz8jpcjce1KE9WaD9zkCaL0+o6xKPv7+91iv16zWuXNIMbJYzDnb\nrDhfCGZ1lVOtpxy0Zr7gZndCasX+cOLUHmiPW9arFRcX53jv8d6x3d4jpaCqCw77A2fna4K1zOYN\nKcJ2e58v7WOWqFlrGQ4d1g5cXpyzXi45Hfb0p4HoHaVWGCl59exzum7g4qzi6dP3eOvxU5pmgZKa\n++2W4/HAzfU1f/fJjzFCTuJLydj3eS59OtFLOyVsa0TK+njnPElKrAvc3N2xOjsjJog+oUqNMoLB\nWeq6yUalYaQsS/QUkDAOA8EHnIs5/UJKgresFpIP3n2b4/01tjvw9a99Bciz7MV8wa/9t/+cb37z\nmwxD4Fd/9Vf4kz/5t4Dmo4/eY71c853vfo/5Ysbl5QUuOP70z7/F17/2VW5vb5Gq4i/+8pt89atf\n4dNPP6U99SQSp5PFu88xRfkmjj5rcnw+gENWYigliCkHD/ggiP8gifmBe/Pw39653I2onM4BEaVM\nfn5GS1mWeR8V84iDn1IVDD/jh/DDA1+WOfvNWTth0NJ020Xa04hSehLgTzEpKZFkou/bTPIaHfNJ\nP9r3eXstpGAcB/ohZ1d552hdbsdmsxnGFCilGW2m+bsxS9Z8cMznDXWpsUPHZpVTWENIFFqSpKY9\nttjRkmNTsrFlGHq0klRViZqi1EWVPfVqco47kS8Z6z0+OpKMeOt4/vIF83nD4ViDiDx9+wlC1wjT\n4BLEkH31y8YznEbKwiATDH335mc2q2d5CRdhtA7nIoiIGC2D8zjv3oDDfZSYMic+kyJKSJIIb8ZD\nztmc2TZvECJrYE+HHd5mhu160TAOLWerOW3XkWzH2ApslyOdhiHrhLUuGO3A9nhEeodUEoSg6zqW\n3uN9YBgt7I9U9Zzr61uWm3OEyJfGYrEiBMfrm2vOz85wIbBcrvjs82dcPX5CAr76cz/P33z3ewzt\nKX/uLkvFHl9sqErDfFZTFIJ28AjZMlvMOX/ylNvDkd46oOGw32VynNFvWvS6blBKY3SFEC3DYPMl\nr8tpZKU5tidOpzxqut/dE4Ln6TtPqMuC9rTHKEnXjbhxoFCKaB11UfDVX/gSV5fnzJoFdTVHqyyV\najZLxPmc8O4FP/fR2/yH7/yIHz17iU8JlGLoO1CK0XlObUdVarQS1PVsSp+WPL9+iTQG6z3WRVyI\nhNGhSk1RVbR9T13X2KFnvz9Sak1Kgbqu6YcMgw8+5Pc2IIRBSsPf/+iH1AbE5NorixJbjlny2Xqq\nqph42AZrBe+9+zalMjSV4mw958njK05ty6ySlEXNBx98kZgS//Eb3+a9995Fa8X3vvuDbMqSfqIP\nmolTElBC5kJMikl3nyteIRUyyWl0kI1SQso3YQ0P0tWU4tR9SXRR4Fx+v8eYpm68QMh89gihp+SN\nn87rZ/oQDiHgvP9JO+TcTwTAZELW6XTK7bHIaESZEv3QUegcN6OERAPj0JOIOJfblKqqiFHjvKOq\nDG0cqXWdHW5GA2Jy2+UWpC4LQkhZJ9v3xKFnPivwY88wDPiQkLqGGDkejkBkHC2D94xj/+YXDOQQ\nRZHb5t5ZqimwcFbnlt71R0RKXCxXVFVBoRXzpsLageG0p1RvE5wl+EAK0I8jVak47e9J1jO4I3c3\nrzmcdkQZKcushhiHESMNoBBS0Q8W6y2mVCAU3ocs20oKh0ejUEJgtGIcPSl6hMpJCstppHA4HOiU\n4Li75cnlBWerJd1pz3Ix59WLZ9m8EEpUjARrCQLw06UzjixXKwZrcS7g+oGyLjk/v0CIfFiXRUnX\ndmx3R2bLNVHkXcD5xTm7/Y721HJ5eY51LlfCLnB2eclfffvb/Nqv/RoiBna7PWerJaf9AwY0JyQo\nXVHVNbPVDNN6ru9byipxOBxYLZdstGK/2/FqaHn58noCwVdZP6xr7m73GNMzjANms8qxTgvJcTro\nT8cOPcX9LFZL5ouGptAomTXvPkZicFRGE61jtVjy5S99ifPzNUVVoqXBmIpCmWyRJzHaI0TLZlHy\nz776ARrP33/+OucWjhlJmbwnBofWC4J3pJTf+/vDgb4fCCSs83ifR06mrFDa0MxmOQnZOaQ2LFcF\nKkHwLrsOY+Q0jJASMYELgtFFfvDDZzz/9EhTgIg5Xfr5s1tOXeB/+V//kNWy4L33v8ZqteGdd9/h\nz/78W/zxn/wRX/nwIw7bI3e3N/zN33yHro+cOvg3//pP6AdHM5txPFq+9VffgukgzPLOXN0/GMWi\nz669B6dijIk3SdHkiz2R3ZHEfFYk0k+WdOnBW5AP6ByVxDSS1JPx6ycZl2nS4v+0Ao5+pg/hRI5C\nedhChhAmp0/Kt5mUfO/7H5Oiz4g+KSB6Cp2301Jkolqp5aQpDiiZE6W67shqs2FZznK1Rpq2yGJK\n3mje3P673SEfJgq2t/eslzMWs5KyEBRGUGqFj5l8dWpPRBRJSrq+IzrLfD5nsZijpMh5WONDxJDl\n1He4IqsFurt7usOBSsJ6MWfdVCiZKKvEfC7ou8QwHLj57Icka0nDCYlBhjyb83Zks9qwP1jsMHB5\ncUE7tvQTIlHEbN82xmRu8K7NIx4R8d5iHWg1Q0pBbUyGzafA0A/E4Il+RFczmrJCEDkeW7q2pTDZ\nxLGYzxiGE6Ux9N0x5+t1J0geGwNKKlSSmIkN0pQF93f32OApgHk9o57Vmc0xxRPtDyfKqiEiMway\nrjh2XU6sSInLR1doKZg3Dbvdbupk5nztFx7zjW98g//yl/4Zq9WSwhiWiwV1UXDa7/DOTsutwLxR\nlLUGrdBaMZs3XJxvOHYn1qs5/TCw2Wzoup7d9oBzHmNKgo+UVY1A8OrVPTF6rA9s90e8DywWDU2T\nL6v12ZqmKekOO9rTSLAj3loqU2DHkUfnl3zlS1/k0dUjVLWgnK2QTIaAkBDJY8oGWWja9p4YBq6W\nNV//yhcYhoFPXtwxb2YEqXM7HT0+RhQyLyNXCw7HI2eXF3R9/0ZPfDz0mZFQyTyOmM/Z3t2xaBqG\nrkfE/My9ePGS+XyGMirL6JIkJoUwms9e7yiaim60Uw4bJFGjdMdoIzf3lr/8D9/i4x/+LSGMlE0+\nGMeu4/rmyAdfuKQfPYPdI0TkcBxpOzi2R0yhuLm+fZPGXGiDcwFiPiQlgihCtlKjINtBIAlSFKQH\nRcODwidGpEpvxhEPoaEx5uw8EQJZgKVyaK8NDENOO38ICX1AniV+gsz9x7x+pg/h589e0ixXGK2B\nbMd1IRKiy6moSrPfbSmLLAMyUzzJbLZEKfUmVdl2LSlGqroihID1jqWS9OPI/pgzwh4/ekRwkeAz\nmPx0arM9droE3DiSEpxv1szqAq0idWmQIrfrTNwCIQt66wkpZti6yDdsDJGqrJCypA+J5CxGCGZa\nU0wx9DOpuLi4REsojaLRirJUlE1iURvkfIVQ6wxfFwKzqjkdB0yhMg/ZFJiioKoheM8wZqvxQ/Cp\nGwZUZQgBQkyElKjKGvCAxBQGo8rcHUwJGXaaK0NACUFVFATn2W+37A4tVZWh9FVtsM6S3IisDHhH\n12cWMUME7ym0Jib5BjQTxo7GSIqyYjy2tO2JU3sEkYhiUgiUFV3boYuGcRh48fwVy/WS9tSyXq84\nHo+88/ZT2jbL8s7Pz3n+8iXKGC4uLri+vsYUJcfDnqE9spzPEZLJ2WY5ti3oCutVDgXVBavlCucs\n7XFPWdc8efKI/f5I3/VolaHoudoSDH2u1l3wRCKnm3uUkjx+fElVF29Gh7qU2b2WcseWYfEKO/Sc\nr9d86Utf5NHVY6SAwUnskOe9Sumc/oLIEfOiRrgS3/XUeM4WDR++95Tr7YmjyykRyhSIKLDjyKye\noY2hH/L75HA4MF/M6YeR+WLBcrmh7QZaP+KHDLeXQjAMA1VZUpkc5aSUwrmBSJj0yBmBGiOMTlDM\na7pjzogD0EWD9AGpekwhafvAhx9d8Pr6Fc2s5Bd/8SvQjxQFLBYL6pmgqGY8f37DF7/8c/z9J5+y\nO5wQpCydlLmzfehG83tI4EOu6ItS5Qp5Ch0g5UgiKRSBhBSZKyxEDkQVMk58b0WUD0iDbOvOmX7x\nzYXywHLJ/+Sh+hVvcuj+sa+f6UP45rrjvfkKrROD9exbyxjLTNd6+YxCJKJSECPzqkaJLM+qi2nh\n5hz96YSYNp6pyA/P0I/5gYiBpqq4fOcdxtHTSXBhoLc9Q9tjjKIsBJv1HKXSZEm2pGTpu4H14oJx\ncIzOEYWhdxkEfXZ1xXF/B33WDXsvOLUBbQxKQRQKqStmOFYmwRSTcmY0pTGkFCAFrM0LtXEEP3rW\n53OaSlOcVQilQGqOh57r6y1xMMigGazFlDov9Y4tnjhZbBOmEuz3PWXZMA47lExZ8xgkUlQURYkQ\n0+hj7HIYaGno7YiUCllout4y9D1ucBS6YtUsqaccs93dPUooiDofBuUc70aCT/R9hy7yz1OJCXxS\nBixwu9+TkiBEcGPPYtZwfnbBk7ee4pHcHY58+vwVxWxOOa9xQ07RjdFztjrj/v6WFAKr1ZJhGJjV\nNd6OnK03vHjxIiddHFquzjZcX79g1dTEFAgxc2Xvdweub/OFq6uK6+2Wi8tLBh+Io8XbgCTy5PEV\nzz5/zluPz7Odviw5nU7UZcXddodzjuUsc0UgcXW2ZpzkTSUJ13Ykr3BdmNQgjkpJvvyF97i4uCSq\niqhqinJJEAbvs6lACkFVCLp2j8JRmYagLN4e8WHkcrPg0fmaF397jZoJlrMlwg8UMrDdHamXS1J0\nhNEyqxdcrpd8+unn7Puey6tLjEksi4pxtNnsUZds7+7YdfmiK4yhPR1pmgaA1SK7+8YxG4OqKnHY\nb0kC6nk+Unp75OLRmp//uV/mG//x3/P+e2/x9O1H7O62nJ3VfOVLH/L973yM9XB7c03dzAg2EsPI\n3/7t9/BRIlRCyInt7T2EvCfK9tfsjBVSoUy2YgefDVCjG4kxoieaolKaFDwpRISQ+JDBQwL5RiGR\nE53z4R0CGFPgrMsqJgnDeJqUEXkGLWUB4Z+Abdm7gDGS4AeMgWG0nNqeZ58+44c//AF4j5ZZExm8\no5rNsvwpJfb7LX3XAQkts/wkpYhEsFmvQWYlhPWe+9t79seO3ubFlFFZfVEVmro2zGcV3nUcD3fE\npDg/u8BbwzCMxBDZbg8U9YzWBg6dw8XEatkQYqDwNXa0SAmvrm8Yxkxim88aHm0alnWNmyAvfdvS\nR58DFvGEFJjNF4io2dseay3NwmBKkSPbkwI0JEOcXEvee0Jy+OBxISF0ltQInRNum7rm1evbPBuP\nkc3ZJYf9Ea1yt7HeLLi9vqZQOoPwp9lZWZVEH2nbDm89lSm5urjCuZ5x6HD2hBaasizQqiAm8N5h\nx4G5qWiqilkp2MxLZnXuXKIfuLndY6Pk7Ml7vPXOezw6O6cuTX5wUsLUNc9u7nh9t2Pwjv31awqd\n3Xyz+Zzz8zP2+x1GKYqieJOkHX1g9d6KkODFs2e8//4XKHTk6nzDqxefYWR++Poxu9tigqqukUVJ\nk+B4OlHVDcvlgvvrW5689RZte+LLX/4QpRSfffYZ7733Nt/61ucU5oyrqwu00iglsNayWS057LcU\nJlf9th/Q0tAe2iyflIpCJM43azabc4pyhiqX+GRIssgjN6VRIhGjo+1H6roieUF73ON9ru5CCJRG\n8/jqjKv7I9tRsdvtWNaGFAKPH18Qyxljf8CQtcjH3Y4PP3ifZ1O8UlmYnLQCFMYgRIUiPx9Zo++5\nvLqk63qKssCHQAg5vLOpC+bnSzZnS3bbLS9f3gEwqwu6057/499+g8IEjocjdwUEO3B33fOXf/an\njHm8zIcffojzgR/9+PNJbz/RD4WajBGTlEzEyQqfsZ8xZUOXKTQyyTxSnPgm3vs3HaCcitgcWZUw\nOle7KaZcNSOnz5ErYVLKxfQ/QK3HmN2iOYIrCwPSDNcD/QAAIABJREFUf8qMuf+/Xm2/JYTLTIFS\n0HUdd3f3vH51Q5zaEmMKqqrK4wICMfj850SIqqqSqizp+45EzJKpsuTUtfmm8xlsAqBUoiwUdVUy\nqyokCS0D4FjMKmKsCF6RYqLvelIME6oyL/KC9zg7YG3F7f0W7zyr9Vk2C1iHH20e/DvP/a6jO554\n+2rF+29nLjNGY/uRFNKUVqEYhx4XSiIp59NZj1ICoSygsQ72uxafFF5IBmt5SImNCYzMyMeEpm0H\ngvBUtSFEi/fQtgekEpxdrDgeWnb7u2xIUDVaS9q2nZaKCuvH3LILwWKzQWvF8dixXtfU1ZLoIyIJ\nQgx5Lhc9hYZ5KVnPKi5WMxSRbjKn3N4fWKzP+OJXfp6zJ1+gbJYEO4D3mMJg7cixG1BFyfmTK262\ne7a3WyyOcRwZh4Hj8URd18ybhvv7O6SU9F2H1pqPP/4YXZQcTycWiwVjfyKmBxfdHF3kB9a6rHdO\nPoJLnCatcl1nM8WsrtFK0k4f5/bmhrPNhsVsxodf+AL7/Z63njxit91mKp6A03FPWRa0x8P03pLc\n3txibaSQef6IUiznG4pqTkRT6jp3JLNV5t66gejy+9rZfAgYqSjqnMtne0dUfkr0ztze1XJF21uM\nlgzDEe09p+Ge6C31ck5ZNxyORy4fPWZWN9RNQ5jGfM5auq6lKArWqyWbzYZnz57RdZNSIHiG3mVV\nQnSQMhGoGxzuuuO/+M9/ia99bWJlaEHXjsxqwTh4ZMwsFYnkfD3ng/e/wPNnNyxmBqMNXTeQkHlE\n1syRUTGeOkQKKPWwGEsolQ1CUuYOeLQWawPz6WKHnxyeanLcPqRvZ5CWRBuBkIHgIUUxecAkeUom\n0SqPO1JK2ZSiJMao6ePkapiUOcw/jdfP9CGMcLT9PkdXGziejtze3uKcYzFfTkBthXMjdpKXhZQr\nvLIs0E1Ok61KjfeTPGzSG2fiVcT5hIoJLQu0DqQikpInxT7rB4PntOtQ6zlNaZB1Q9+7HO2jFKbU\nCKUn91LAuUDXddSzhm4YkbqlrGoaU1IUOS5pGCxFWVKbxKFveXl3D8CXv/Y17q9fctjd4m1PoRQi\nwtBmXKYQOnMJUkBJgy4q7Jhwg8CLxBDyxj3lCF1CTOiU0YS6mGWUpcuHdNed0MbQdp71+oymqUgp\ncnvXcXa+YTx2lFpy8I6qziYE+yDkVxKl4LC/JwSHEFV2fyVL8olxGClURCtYLmastKTW4IaOu8Oe\nwyG36E8/+JC3P/iIyydPKedXIGra0y5DVGSk0AXeW8rCo0zB9phHBtEFVusljx8/pjAFXXfibL1m\nvV5jjGG3vef8fMPh2NL3Pav1mlc315wtlzjvOLu4zMByLaibhiIq2jEw2kDb93hrefn6JcvVnEeX\nj/Ay0rcdi/k8u9PmCx4/fsQPvv99zs7PqMqSYWg5HfesVys2qwU3120OVa1zC7/b3k/uz7wfSDFQ\nNSvW60uqckkImtFClAmFpqxrpFK03qFVwaI6o28PaC1oqiVDf+R0yJQ05x1KCYJ3nO53mKohpcTZ\n5pwxRoa+IwbPQSaCH5nPZ+z2e9599x2+893vsdpsMEUOjA3Bo7XibLPmfntPURgW80t2+z1lZaiq\ngqurK4ah5+bVy+n7HpESfvzjv+df/Iv/AYDf+J//JS9fPme3veXb3/5r/sf/6b/ndNjx99//Q9aL\nAnxk7Mc3MPrt9sB+f8pg/bYjiIIoFGliVuRghIwDHcaewpSTaw0gB8omcpZkmJ7vB073wwH+k4+T\nGcspSmLwhCAmJ2sEAt4FpMyohJ+YtrK2/Y2aIv10lnLwM34Iv//+U8pSYb1FaUHfn+i6lpQEu92B\nuilpu266pUDI7JaJMcuQVsslKXmGvmNztqIsa7b3Ow7HjhB58wMOISIlnG1WBGcZhp7FvMkEJhFx\nY4dKDm00bdtBylBwGwI+JIqyZvSButZYF7DOopyhmS/Y7vewPyKEZDZbUDYzkMW0xTck35AmUtYo\n4J2PPmJsr2h3d7SnPd56IGR4jgzT7ZyIIeDdgNZZsO59xKecIO19JPlcacUo6LoRUwx5Fl5q7u7v\nWa5mgMD6gDGCV69eEEIWuI9jh/cjddEwn9UoU3B3t6OuDPOFQUuJIFCXhrPzCwZ74n5/pCkarHPo\nSZFSKpgVGmE7bBi5O3agBBePLwH4+i//V6hmiVAFnU346DKMu6zwvufQd6hCcXN/z+cvX7I+O+fm\n9V2eVRc6b6xjYjab8eLFC8oySxbHYUAgeOvJE7757b/mnXfeYRxGqscz9vcdQiWczdzoCa9HfPOQ\nRqwdKQtN37ds729JMbfpm80mR58XivttTm6WkokBMXB1cYbWinFokVM+mbNZE13qgt6Nb8wEWkjO\nzi5Yrs9JKEIAEQXSFByOJ5abFVUzox+OKKEYx6xkUUEyOsvoHLIoMCIR45GmKjnfLNiOit4Gdl3P\n2VuX3G/vM4FwWmwPo8e5XV5uXd/wtV/4z/j000/xTmQZprPYoWd7f4fRmtLU9H0PMRDsyG44MY49\nVxdnPHlyxX6/A275+Z/7Cv/qX/1LHl3l3+0//9VfoWtbTu2R/+6/+ef85V/+GR9//DHLueKwO/HH\nP/gLpEooZdhu99ze3OasOgRS51DcMFqK6YR6+N3IidmMyAVS1vFKfPgJWxzyn2oC1z8s2qSUCCkm\nzkxESIki27GlBDMB75VSkLKMMASPczB92J+YPNKDzvgf//qZPoSfvPWEu93n0w9Y4lxge78njB6l\nDNa6PENyluAcdbMBkfXD3nuur6/zw1oaTqcTx8OJ/f6E8xEhNULkOWhRFNRVBqHUTUNTaYwWKJFo\n6oJUKZQS9F1P8IHjqcW6gCkrnI/0Nr8ZhtFmAIsxU2yNenPIa52/phgjUuQIHOsTY2+Ri1wt3R2y\nTfPRZsNqvUZEj+1a7u7vud/eZz2y9wTr0DpvzAMOobJkx9l8S1sX8DEitcZUifl8kQ/mKXE6j08S\ns/mcwnu22y12qrhMWeC9Y+xOCJl4//0P+PGnz9FasFwuWC0aRPIc9nuS8MwwbFYLTl1OJCnKgjQ6\nZEpURjF2R4pCcTp1LM8WPHn6HhePcghs0FWOjVIls/kaZ6E/eUbvGYcx6zunZJOLq8f88JMfMY5j\nTkRuaoQQWDdydrbBjSM3N9cIIbi8vACyhvnxo0fc3t7z0Ucf8vLVK95560nWUyu43+5yIoZPiJQV\nAW03EJOnqWt8sMTg2W8PPH70GDdaFrP5G1clMWGUZnE2z5wMNzCrF/SngaYucdbibSZtBWsnqmVC\na0VTVLz77gcYU9EPDlVVRAFvPXnE+1/8iEDikx9+TIyB3vWIYBEiEkNgDAHnHQnB6XRCijzVPOyP\n1OsrFpsl7X7P/tRyeXHJzXbLxdkV9/d3uBgZRkd5zAVNXRWURmOD53jsMUZzefU4G4z6nhCyQ+7R\no0tevLTMdM049szqhmHsCM6xXMzZ7bb83Q++D2Gy8nrHvK6YNzVPHl3xzltv8/r6mj/+oz/KbXz6\nEV/66gd8+6/+mrubW+ZNw+iOVIWelDGCuhColA/AjBXIdDmIU+GkJnerIIbwhv3yEPLwUAk/AMBi\nzPNzbQxSZXVECnKqsEEbAQRSFJRlxTAMWRM+LcsfDmApJTEkYvgnYFtW0hB8woWA0SWt9wSdEEiU\nFCitMUVBCI6+6+i7Ea/kZEXMt6pSBjt69vvjPxBgG/pxRBCQ2mBMiVaC9nikKnWODyoMKVpEyPNN\nlSQaASH8Azt1Xno5H0DkA05LiVCCMQaid+iJOWSUoKkrvPMg8oIiRo3Uhu0hz0iv9ydkYSg6y7zU\nzKqG5cWS87ef0Hcth/2e69fX3N7c4EN4s5T0KSBjnjsONmRbdVHw9tO3GexI1w+MLkNxnr+4oaoL\npMxyvRgzzDwxzc2U5u72Fc2soCjzTNpam2eOztLU50jhiK6nNDUhWtrO47ylrha02xPax/zOCiJX\niTGyeXTFF7/0VTYXl1DMABBFTTtGZBkpnUXL8v8k711jNcvSu77fWnvt+36v51Ln1KW7p8fdPYPH\n9oyxYyCAnJAogJUERyE2AslCYIEUiBQU+Uu4GEISjW2EIviCv/GBS6R8cZxoHKM4gIiNPbaxGbun\nZ3rc3VXVdTu397bv65YPa9exje3xWCBDNOtT1dlV9e4673vWXs/z/P+/f4gm2m/DBjtqhPc0TY82\nfvp++/CAEj4oFJKU7Tac7L7+67+eL3zhCyznC6wZ2E8zhHK+YLk+Ik4SBmMCD8MZnBdsdvsQRT99\n5rz3ZEnKbF5RVgX1oWY5WzAMA0+ffngbxXV2doaUUBQZ+/2e1aJitx2pypy+bZBChnifST4pfABM\ntV3gZ6TxjKKoWK3X7NuRKJY4DO89+hJRFlFVJR957R7Cd+i+pq23KBEhhGZoR+wYKhu8ZxxH0iRj\nMct5dnPD/ChltV7T7bc0TcPZ6TGb/Y5uCKknwzjSjyPCGXa7LevVkueXL0Ku3awkURGX+y137pzx\n4sUL9rsbpHAcrVY0Xct+O/L222/z2quvUFUVfd/ijeWzn/0ZjpZrAHabDSqOKYoSZIDW33+Q811/\n8k9jTWAzZFXF9eUlZZ6x32/5Zz/+4/z8L7zNF37pPa73NWoagGljwU8nWBkGhUEiNrk+pUJJFRyX\nvGQ/yNusypebMAQ7s/MObx1h6Pwrj7OhbYFgAnmZUGEHsn34E9PxNwD5vwp6wttNDUSoSJIkKZH0\nt7bkKJJ4gpA6ZNElyEgQRwohPM4bnHF0bc9BDywWC7yHtglc3aqqghNGyqCHHS2raoEQFsxIV49E\nkUWlKUpGSB+kZJLwGl4Ej45D3J6oX75ROE+exUgRQRJPpZAKbygefKCHWT0SqRQ76Q0/fHrJi4sr\nyizhtbvnvPbKfWSUMQ4DXsTM1ycsj8940zmePn3C44cPOewPKCWJfIx0IC0UZU61mNENLW3XgYwY\njZ7g7Snr1RFKqfAwcGHY4kXQa2Z5yc3VBetFhfESo0eGIeQJjsNA2x5o6i12HFCLCBEJ4jhisT7l\n6vIGbQxeW0Qeo3CkseL45JwHH/kIRTmndxIVBYegymdkaY51nnHQjLonj2PyPKGtB2ZVyc3ugBk1\n7/3S+8hIMZ8HGdrdu2cc6gNKRpydneInrsjp6SnjODCrCm62O+7cucNgLD//c5/jwYN7xEXG6ugY\n7zQvPqy5vtkwak/f96xWR0h1QAjPcjEPVnMpiKOErmko8jxgGbXmw0ePgj46Tbl7fsbVxTMW8xKj\nB5JEMnRjcGBNA3Q50Q3E9O8JIVgsFiyXK1ANrdEU8zmomEePv8Tp8TFPP+w4Ws65rlusaYkTiUTj\nbIu1HUgwYzAuSRkcl3I3cnHxnK6aMS9S8qxkvV5ivOPqeoO2lrfeeosPH71PoiJ2ux3H6wXLReB8\nWGu5uroMhLjrK5SKmM0qhqGnLCturja8/vpHefL4EZeXVzijuXv3jJubKz7/i59nOZvzHX8KpFSM\ng0YwgoyIU0Hfm6BPdwHaI0fLfLag72pOTk74I//pt/Effuvv58f+yT/mhz/zIzx9egVpQhQnxHHM\n9dWWKJLEScjpC2xxR5qkxNPw7qU65qUy4qX8TOtgi1dxTKQsQgRNdcjmYxr2iWmoHU38iNAueenC\nC/3ml4qIr5J2xE/+5E/zykdOiLOY9fqE7fVTQEysBhe0fZP0TEVqkrOEp5YUMSoJEqtEJQy9Icsy\n7j94BW10AMR7h3GOvu8ZuwFrUrJU4TykSRLMF/6XORV60FhtEV6G9oMZ0S6kFIc12R8B4RxxqoLU\nZZroCjx5VTCOhm27ZRgd2gWQEEBezdFmoO4Mj55dMwye06Mld84WZEkSeofDiNWGk9N7nJycc/Hi\nOR988B7NOCAjhXMaFaehEhCQFzlEEVGiePLkGXGSURRlaM8cDvQ6YD6t92jtiJMu0Op8UH60XXdL\nAvM2cFcXixlpvCARMe3QIVU0ncZS4llMe70lz1JOTypOThYsT++T5BW7TuOihHUa2i9eRkgV0IyL\necr+6go79hg9oHVPnpe0TcN+V6P7kfmqpKpyXjx9SpwE9vPd83O0HiZjjmW/33O0Wkz97YimaWj6\ngTfe+BjWhhzCd997j7t3Thm1piqChNBaFyD4WiMlaK0Zhp4szSiykjhWaK3ZbLbMqpJhGDg/P8d7\nT1PX5FnQ9W43N5RFSZrEOG1xNrQj0iSh1xbhg15KTLb1q+sr6qFH5TnVPEdlGfWu5ud/7rO8+cbr\nXF7sKTNFIgvqwzUXl89p65Cx13QaYSxxFCy9H/vYW1x3X6C+3nM4HBjbhsW8YDavsMaSpRmHQ83D\nxx9yenKK6WqcGTnsdwil0FPPlYkjPAyeoshJ4kAJVJFiPlvS1C3eC4q8IlYSPWiMttw9P+NLX3of\ngKdPn005fII4zhiND8kUcQbWsdnWzBcCZ0bKLGNoG9I0pspj/rM/+B/z+3/Pt/B//siP8pM//w6P\nX1wE3W8cerpKBYaHdQ6pAwdG60BXg1DNvGR8iF+xU76UlkHQ+rrgIWICM+K9xE0/Ny/bDkEb/NLi\n7G+t0lqbW+fcv+76d3oT3h466re/wL/3u34nQ9uxmFcomdGLYE8OTNCIOAqyEm00znu8NRg9Qd7j\nUJZkec44DtxsbwJYXUq0NlNrQSAjQdN3CJnizBgskE7gnSFRIbVAxQlx5hDEeG2xfgQReLtSiCku\nJzT5DR5vQzbZqDWxSoiSmGHQGOOIkxQ7ZZMVZSjPjTbMqgVWDwzDyPOLKxaLOVlWkiYpzoZUXYRk\nd9gTCXjtI69TlCVvv/NLvLjYgNXEeaga9NhhPMRJxnI+4+mTp7zx5ke5uLjiUDeoNMcONcPYI4VE\njweEEGRJiK3J05zt5oqus5wclcwXM4o8J00keuzozIh1jjKOefzkKVVRgYVqnvLqaw84XuYUZcpg\nJX0zUlQLiENmG4QAx9E6xtGwv7mia/ZICePYUx9qnr+45MPnL7i4eMFyuaSsKpaLGc8+/PBWhvTu\nu+/yDd/wdRz2e54/e8psPme/r7noOmaLJZESCKl4+MEH06S/DJtHnHL//qtcvnhOEodK6xd+8RdZ\nHa2J44Sh78nzIqSaTPS41WoVHjZpepukPY7B/lzkCVfXuyBRUzHaDHjhsT48wOIkYrRmOrE54iyl\nHzp21zVnD+6za1q6vmeWZ9zcXBJJz8MP3ueTX/+1NIcN7WEb6GZtx363R1uHlDkRDm9BZfDG17zG\n9aHFi0f0o6VrO7b7LdlNQtN0rFYr3M0NYz9wdXlFlSliGZyjJ8sFH374IcfHxxR5yjCZdfDhga7H\nkcP+EOYvVlMUJbv9jixNMUZTFiUvrm44OQ79+OvtDcdChBMkHSrNONQtxXyJkBEeydMPn5GlMcwr\n2sOW9XpOHEtiJVku5vyxP/pH+cP/ueOHP/Oj/MRP/Di6bxESpLChwvBTqEOkSJVExhJjmapTMVmZ\nAS/xLkL6YOCQwmG9CG45Y7DOoKIR4WOwDi97PAIvHA6Ltz2xcERS4qXHOM+oBHH5VWDWGGzM9eYJ\n9W7HyWoGekdzCMBpqYKHPcHhLOixDe6WCbqh4tAvjVRMpGDQPV54Rj0G2dlEt1JKMfbDRGWSaDSR\nkjg8zrqg1R3GMMiLFE45jPEYFwJOpLUoF9iikQzyrVEbnFB07UCUKJx39KNF+ZC2gIwoigLBSFHk\njFODP1aKDz94n7t3zynKnOZw4PL6EiUHTtZLiqKgKDJwMVQlXX3gUNdU1YxPfPxj5PF7PHwYjCGp\nDMCSNE1puoZHVxccLWYc9js2uw0yCszjYj6jawbMMHD//j2GtmF7c0WanFIVM07WluuLHVWuODte\n0vc9Vxcb8iK/rTyauuV0ucSMGu8HvuVbvpG7Z2dcXV7gdHAljbpn12nu3X+FWRX6qt45rAnBps+e\nPMK0LbPZjPXqiLpu+YWf/hm0C1PwsR159SOvcvXi+ZTtJijynNVySV3XvPLqq2x2G4qiYBg0Ly6f\nUFRLLi5ekGVZUJD0PY8/eMjHPhZOxTc3B7J8BoD1A6dndxjHkWHQQTvuFS+loEop2ra9LW2XiwVa\n6wDEH0currfIKAMZ0XQGayyD1kRTn9K4ESVDjplKEqwbuNhdodKUXdsyWyxRMqXdDayXJwxNw2q1\nxGhHP1i0FRinSLMl86WirluET9kfNlRVyjCO1Psr3nj1hHp3yaYeiU/XbPd7+r5lsZhzdHRCEkVc\nXV1SFQV1vSXPU9rRsN+1ZFnB5eUFeZFw9+4xIGnbHmcFSZKz3x3wTrPZHlgfr5nN5/RDT681Yx1C\nSs3k0/5/P/vjfN0bv4OPf/Qt+rohSTSxiBgONRpPkuVkKkP3I9fjlixLaXpPYiFJZABLxYpESP7L\nb/vD/MFv/b28/cXP8+P//Cf4mX/x88RxjhSSIo3JE4WwXYBQeYFBoJIEZTXCv+T/pkgpiNDISONt\nhPM5qfIM5oYkgUSOeJfi5YgRAi88aQbLMiWPLApLlGZ0Fppne+LiZVbiv976TTfh7XbL93zP9/D4\n8WOSJOHVV1/lr/7Vv8pqteLnfu7n+Ct/5a8wDAP37t3j+7//+1mvQ2P+y137im9OeMygifAIZ1AS\nYiWRHqIkJhKOyDu8Cgi9l5HigXoUTsACyTi0QUmRhDhyrQ1pmuOco2tbtLZUVUqaKvr2gPAOEUWB\nkiQEyAjnBc6G/luve9quR4/hFJem+VTqBCaDFwSWQ5oiVBxkZDKQywY9BvgMMJ/N6I0mLUOP9Orq\nEqUUJ8dHzKqK7faGs9MT8D3PLq6pqo6j9ZooCozkOCtoh2Ajns/mPPjI6+yajtlszvV2j4gcUZzS\n72qq2YLzu3e5vNmwmM/YHToIWHmUkkgfrKl5mnD/3t2phx6IYvN5zsnJ8e3U2fugBS6KhOVyGSA/\nbcPRcsE3/85PMXYNX3r3XcoiDxS8THJyeor3/CrZlrcWbyxYx2q15PHmOiA+kYxac3R0xLPLS4Zx\n4PTu3ZDPF8dkWUbXdbzyygOeP3+OlDMeP37M66+/zsOHD0FAFKmQcVdVgYOQZazXa7IsmxjPgqqq\nplLbBkpZFHqkeZ4zn88Zx5Gr6xvKPLSLXpbBAJFSOB1srd574snFJQAzhugoIcTEHgn8AYlkkJ4k\nVoDjxcVz7j14ZWqvhVbK5dUN5+enpEVBNZvjpCRSCi8UKk5RiUa5glLG9K1BJQl+khoeDg15NUPF\nMW2z4Xx1graW/WHPdrtDypAMfnx8FDIYFwv6vkUbw83NlrOzEPtU1y1lGVpGSZKRVQV1/QwExHFK\nNRNIGdN1A2ma0bUdkogkyRimz/azZ89pdw0YODs6R2sfwFYqxoRCliSOadsW5y3elwgBfW9xzpLn\nKYKIPAvhqMv5gv/gW7+Vj3/8Y/y+3/cl/q9/9GNsdg2PP3yK8Oa2PxvoyiJYlp1GCgUERYXHMrqR\nyHiEDPMQ7YOPYD6HIklRviJRkn3XM7YbVmXB6bpCmY7IOxApetBEA1T/hs6wvymZWAjBd3/3d/OZ\nz3yGH/qhH+L+/fv8jb/xNwD4nu/5Hr73e7+XH/mRH+Gbvumb+IEf+IHbv/flrn2lS3jN/bsn6LFD\nRSIwYbHkSUQiHUo4kjjEmUgcWZqE0ZgTRCIiEjGSCOEV3obYoHrfsLneoXuDGRxlPud4dUKiUoYh\nyKKCM8bT9QODtmjrsSGsg7YbMNYxjIamC3xV57mFC/XjQD/owDWNQ6BlFAVbaNu2jGOQ0NRtG5x0\nfc/YBYra0GuGfsQazWJecnq0CrAZ6zEebrYH3nn3l3jni1+i6UM/etAWGac8vbgiL+e8+bWfQCPY\nNTV5Occ46HrNoC1Pnj7HWktbN/RdMEy83FTni4rN5obddsPde2ekWcLl1SWRknzTN3+S45MjNtsb\n2q4hzRK0GYNOWXrOT0/5HR//OG+98QZd0/Dwg4fBXpqmRJGY0ic0RZHSdTV6nP6/bUuz25NGCgnM\nFzO0GanrA33fsz8cyMsCFYectyxPqQ+hZaL1yPX1NW+++cbtNPz58+fEcRyoby5Iq172hq21vPPO\nOwghePToEfv9Hgvs6wP9OJAVOQ7P+vgIlcRcb24Y9MjJycntgKfv+2nDDnOEpmmo65p2ei/HcQyp\nyON421N8iS/N85yyrJCSkIadxHRtE1jVOmxcUoYoIessRyd3wgnTGIiS0D9PUnwUQxSTFjOyMifN\nM4gEXgj6UbM71MxmC4qyZLvd0rYNs1mF1iMXFxfs93uMCfMRpRTHx8cIIfFe8OzZC0Byc73j8eOn\n7PcNV1dXPHz4MND/jGbUFiFCrJQeHWmSo1SKMY6+7dhttwCs1sccn9yhbjuGccRYRxQpnLGYXk9c\nah1COq3HWsd2u2e/O9D3I12nqeuO/XaPFALvoNs3LKsZv/d3/W6+9y/+9/zXf+a7ee3eXVbzGdIL\nvDEBZ+kdwo8IOxILQyQM3mrKPOboqOLoKOPe+Zo7x0uEG1jMEu6dLblzXHG0zCmjlEQLUg9Hs4x5\nJiljKOOIXCoSp8gsLNLht7yn/XrrN92EF4sF3/zN33z7+09+8pM8ffqUz33uc6Rpyqc+9SkAvvM7\nv5PPfOYzAF/22m9lna4XvPn6K2RJHFoMkSCSDuEN3vR4N5ImwU2nVKAj4TyRlAgPYz8yDiElI01T\nYpUgkKgovmWEvkzPaNoGpeIQVdMPaGMxlpAkoB39aGiGnn7UDFNKgUpS1K3gP2R7Wf8SGh0Fpmzd\ncqgPbLZbtvtDAGMjMMay2W5xxrLZTI65Nz7CRz/6ClfXF3zhnbd5/uIJ73/wHs+ev6DtNVZIDk3H\n9WaP9RIZJzgi2sHQD4a3v/gFfvGdd2j6kbO7DxidZ3uoqduB3b4mSfNwwp2YqtZaEqXQYx8cYWWB\nHnp2my3OOS4ursPwru9J05RXX3311pUWyTAO1uPTAAAgAElEQVTvf/zwIc+ePiFWCj2GjTFOE7wI\n7IHlckmeK5ww5EVKWWaYadNxzjH2A4fNjsvLSy4uL2i7FqkkbVdjveXZs6eUVcH5vXMOhwMIwemd\nO2z3O+7ev8fzFy+42W6YLebIKLR5rHfcfXCfi6tLZssF9199hSRLufvgPoPR7OsaS8guy7OScTAT\nlrMjTXJilXJ8dEqaZNxstrdQ79lshlLqdoNv25ZhGGjbFmvCyVep6NadFcdxMBYQZFJRFKyuIe3B\nMow919cXCOmxTiMjePOtN5gv51SzCpWkjMaCUuybDidjZJJjiRitpR46DB7toe0119s9fT8SKUWS\nJHjhb3vnx8fHvPbaa9y5EyzyLx8ceR6qOCnCjAQfmMllMWO7CVXB6Z2TSdYoWCzWaO1o255ISLab\nDVYb0iRFesjjUNW1XRfyH5MkJNhMuls7anAO3fV0XXcrHzPahDgo64ikou8GmrqjqWu6OlAQVaTA\nQle35CrhjY+8xvf9z/8jf/a7/xRf/4mvI0+zKcPOcrRIuXtnzr3zFceriiwVrOcxd48zTpYRp4uU\nRQ6ZGJmnnkXmKERLRs86TxG6AwupMujxhjgekQx4q/GjxmvQ/W9TO+JXLu89/+Af/AP+wB/4Azx7\n9ox79+7dXlutVkAQyH+5ay91li/Xfr9nv9//qq9FU0TQ137sLbruKWIi6S9XS/QYwBlxIm+TVZO4\nCH1hHdQM1gY1Az5MMZ0XwSkzWRnHiRyS5TmCgH0UiKCJ7fuAmZSSWCVYF0Ai2lq63jA6R9MOCFQI\nwTQegwGm5OUpFknGaQgKlZK+HzDOTpIaj7GWOEkpkgLvHWkS+pLL5ZyPvPYKY9egFDhneP+992la\nA03LMllgnGez3fOTn/1pjo/WqCiibRsWyyXFYs7Z/fsY67i+3rKtO242W+I4IUkTrAsKj2EYMYND\nyIAoLPKMseuROMqq4OT0iKHruHv3mNlsDngOEwNhNqvouo7j4zWR14xDR9u2fO5zn6MqC4o8m8Im\nBSrN2exrFscLmqZFiBsWixXR5BAchxAYeX1zhdYDxmhOz07Ji5ybzU3YyJKE2XzO4XCg6TpkrEiz\njLppadqWxWpFWqRsdlvu3r/HB+9/wPHxMYd9TZwkXF1dkaYpy9WK58+fs16vWa6WQKgCXjx/RtM0\nZFkW1CB9hxQymFas5ez8nHYfCGlFUaC1vp2Qv2QTvIRDhdzA8HDJsxTv7G36grUWPRjSKbEkThRY\nR9PUHA5bhIy4/8prCBmjTUhgllLQNuF7lBUFs0lGduhazGBJi5z51OfXxiCjmEPdIVWK99D3PW3b\nUc2qwDvpOrwXvPrqa+x2NwhZ0HUdy8WCZh+IaXGsiCKJsyFJe7+rMWZybCIYR0MSByWKEGJShiQU\ncchzTNNQ/TkTJIdN22CcxWGRShDJBIcIaM08mdKpHcMYLPHjqNF6F4aIk8stz7Pw2oNFRYpYCvpW\nh18rwRuvvY791v8I5/9v/uUvfo5USVZljPKaNInwQqCHlipNOM49TnisbZjHJUo7CmmZxZ7RDais\nwI+GcexREcwWGXES4boaox2j6xm0hRjmxyEl/dmzZ79GMzyfz3/NXvcbrd/SJvzX/tpfoyxL/sSf\n+BP86I/+6K+57r+MZuM3uvZ3/+7f5W//7b/9q7527949fuzHfoy//r/8r7+V2/v//fqhz/zUv+1b\n+G1d3/0X/qd/27fw27b+t3/68N/2Lfy2rv/jH3/xt/X1fu+3wZ/+7/6H39bXfLn++B//4zx58uRX\nfe3P/bk/x5//83/+K/r7X/Em/OlPf5pHjx7xd/7O3wHg/Pz8V73wzc0NQgjm8/mXvfavru/6ru/i\n27/923/V1146XT79F/8kHzz8HNe7ywDhri1KJhRpQhIHqYmchiFSRkgRY01oJ/R96M/1XY8jDq67\nOLQ1xnG8HdwF27PGS6ibBhVBIgXCe4qiwDtJO2q60TCOFqcUXkq6diCOc6zxt8i/oCsM+XZSKEaj\n8VN7QkYBr5dmBUmS0Pc93ltWyzlllfH//NPP8V982zdz7/yUPFOURYIZe7bbHfvGMfSay6sX05Bi\nFgZIaUpZFlRFGUptq+n7ng8ff8hyuebxoyc8fvyM3c5wfFxyenzCZrsNhC7jqBYLPJKmPSC942s/\n/iaH3YYsTXjvvfew1vLgwQPW63U4JbYtl5eXvPrqq6GnGEUcdjtEFAhySRpOYFEUMfQD5YQWXZ0u\nOOwOLBer0LMdNX/9B/4+f+HPfDsXF1dTX7Dn3r0zcPDB+494fnXNYrFieXrCzX5PpzWL1RolYtqm\n4dmzp9y7d5ejozV3zk7J85zdbsu9e/f47E99lq4JPdvD4cB6vb6VJS6Xy4k54dheX7Lb3nBzczOZ\nNVa8+eabtG2QZ0kpuXd2h2Z7zWZzw2w2C/38up760vr282rxk6TLIzw4q4kjSeThf/+JD/kjv+ce\nSRySe/M8I05T2rFn0AaVlQFLGmU4L3hw/x5lWZBl6aT2CbmH4zgipeRwCFIxqXxQFez2XF9esbna\nMOiROMn4pYcfIGRIDy7KMoRk+mD9nc8riiLD2AFwzOYF2+tL7t+/j3OCx48+xPuX3AUoqyyA641j\n1CHuSAjI44hg89WYsQv2aQn/6Kcu+E9+5zGnp2e8/vqbfMM3fJLTk3PyrKLphwB+F0FRFNKX1eSQ\nDNzqPA9pJOM4omIfUJUokrgkzyukCMP1cRyngWr4/Fk/8t/8t38WZ0Y++Ylj0A3GRFzWnp99+wXf\n9IlT7q9GeltDtObmuuCnfvoDftfve4XVMSgVEYsFdoj5R//sZ4iXC77um76B0bT0uxtcrzEjfOn9\n51w3hk9+y7/PP/vMP+Hv/b2/9+uehL/S9RVtwn/zb/5N3n77bX7wB3/wdjr8iU98gmEY+Nmf/Vm+\n8Ru/kX/4D/8hf+gP/aHf9Nq/ur7csf3Q1Bzals3+wDhqsniGdY6278FL0jgMM6JITWJrDTjSFJIk\nJkuhzwTdAPQB8COlJ0mjCYLkiaKwgSMinPdEwqNEmNxb43E4nBdEMkZIgkZwHGm6jmxyf6VZHgAj\neJDB4YeMUAiMMSRJiopj+nEIwzijKYqK5awIzOLsZd9Q4l2YVEdiSRQFNUGWRNT7A4lSzIrQQplX\nJUkcIYVnPq+IY8HFzQ0g0OPA1cULIulZLSteuTcP+XFTbby5qTk6XtLWDXGW09Qt986O8d4RKUlR\n5pydnZHnOWmaorXGGMPp6SlHR0e3fVGEpJov8SKkmvTDQN8PSJlwfHYc/u9VxbMX25DYoAa6fjNl\nf8E7735xMgc4+rbmwycWYwy9HlivFiCjwOJIU1SevfThYJxFKklW5KHUNIZcCKSKuNluyIuCoRsn\nroCk6zpms9mEQgxDujxLSZJkQpEG1Udd17eHh5e9ykePHiJMAJi/HGi9PCS8hJxba2+xpuMwIKd5\nQ5IkDE0LQFVVoe0lBHmRo5IY7S39ONI0NUImeBFIbn29Z7mcURQlSZJwdnbO2LUgQp6gcy4wFKzH\nWI2fkl3qtme321HOKhaLBUJKumFkHMbpflKkVBwOB5JEMYwDSkVkWcrdeyeMuqEslozjCMQUeYXz\nFj14xqENTF4RYrvyLGG+CLB4ScrQQRR8HgAsioy+3vPho1/i4x9/iziPScuYKI+nh4GZCGa/TCrr\nunCQCQGeFiFiUJZBD6RpjEpStPYTCU2CT4nkZF92hrZr+WPf+V/xCz//T5F+RyQtUSzxVoOHrCzR\nEuL8GMOCnbGoxQxfndPFI8Zpur2h39Y0TqIbyT//l88wzpAKi25qVJRw2YFLMnqmcILz869kG/0N\n12+6CX/pS1/iB3/wB3nttdf4ju/4DgAePHjA3/pbf4tPf/rT/OW//JcZx5H79+/z/d///UBQVHzf\n930ff+kv/aVfc+23sowx5EVJ0Vf0445+1FRJEXpt04dcOI2xGiy37pZwMhaUZcxsUTDomLrpqOsa\nN0HThyFMsD3BJRPISgQRuPC3NCbrHNoYhEwCLU0PjKYnimN6PSJ0GCREUVBVaGsYdJDK4WEwGhUn\nOO8Dfg/JoWlpmhbd5ZR5SlWFJ/+LZ8/BDgx9jRQWZ0a8F/R9YE+ksSKSAj0MdG1NsVoyDB1dsycv\nksDLnU46IfkiwN6Ft8QTPaqpG5I0QO5HY1BSkChxWyFYa7i6uOD4+JR+GDgcDpydnaOUom5aiiJH\nyoiymuEQDKMOpxrrKKo5WQnjOJBXM9q2RaiEar7msN9zvdmz222pqmBOqbuWxbzEeViuliEFe3pf\n0zQLfFmnkZFgNIZoGvb1fc98Pp9gPkFiVpZliFfyjvsPHgSZm9FcX99wvdkwGsPJyQmb3Q6lgtoi\npCZXnBxLpFQTI1qwWq24vr4OrizrGdsOKadg1r5nNpuFFGu4VU6kSUKWprQy5J5FMiOOZAh1nf4c\nUUSSqKl/HHq+cRyDE4zaMZqeNC2IhUf3LTdtQ5Km4C1N0yGkZL/bcXJ6yq7eU6wqijQn8pKm69DW\nMJstyIoc0zbkRYqxjq4bmFVzmqYjuMXCYC5SUOT55ITLuL66YbO5oSxLoijjsO9CtH0kMDrwLZqu\noe9asvQIFUnysgCnUTLEzTsdFANj1xJnOX0XGNXWabTTyDhBiggzGOIoQvkQ2qkiyThCpAQhdFti\n7Yg2mjhLmc0XOBNhbTBZWBP4EUIFCZqUEavFkt/zu383u6svsbseibxEqownmy0ImK1PseYan0i2\nh4ht5+lFxTuPb9Byy6hHhEmJBsPBKFS5YDdkqCTBeY2PEqJMMdIiRYZz1W95T/v11m+6CX/N13wN\nn//853/da5/61Kf44R/+4V/32ic/+cnf8NpXurIkI48TMhkT+4hIKCYDEoO2qNESeUmSZHjrsGPg\nSEgfPPoqFiQokjIjSWKSWLLb7WmbBqbsqikwjDxNaZqeWEW3KgsvwOpQkmkzMFho9MDgHN6KaRg4\nksaCLMowOtCtrB4p8pQ4ishURCQMSaQQSlAUCScnFV3XovuRtt7dxv3kqSJWiqfXW6RULNcrjHNY\nDBpLXhU4Y7i+ecHRahlsnF5hzYgZBpSLIIrY1wf0aDgcaq6ubpBKcu/ufXb7mq53zBYLrHWYsSM7\nqphXKUksKMswqGmbnvNTQbFYYGYznjx5xvn5fZJ8iVAZPkkhLjkcXpDmYZAjhcRbh3SQJhV6HIJk\n0FqkE3gLehhDWsfEYs3zCBkF5oFwIXstK0uwJigdqhnPr6+QMiaRktVqibWCNK0nSZghy0rquuXi\n4pqTk2OePHnCcpmGPDZvWc6D7nMcDbOiYj5foI0mTyvmk3Rru9ngI8HVxSWX11fAL5eT280NUmVo\na7D9EDZ/rdHaUKYJdnqoYx3WaJQIw+FIhLbFyUlAO/baILwji0Lacj+OxDImVwn0A0JGqBi8GRiN\nZgSOjtbkeUHXNKQqZhgGjlZLcBblPUNdM9ZtkKDZkY+++VEePn5EbwdEFLTM1nmSrAQZkSRxqByl\nCyQLJ7i6vGa1XKIbzxuv/Q7efe99ZOQoqhmHXuOJmM9n1HVD02zomi2LquTeUUUWaYQZcEYT47De\nMZjwWW6sh7ZnHme8/+gp5/dveGN5Stf25GVKO9bE2bT9OIExniQpiCRo3YdQXqFpBk1WLpAiCcGb\nztK1eyIpGcaR2aIiiRO6NsDj237g+OwtvvDeE+pmJCkT3nna0QnFP//CC6IM9jc35OmKy4sD1ke0\nrcdEJaMviVSM8i3G9ShpkPGIk47BGeIU+qFBoVFJipFfBRQ1EzyIKCkpsxyjwRlHkims13SDIVcZ\no/bBfmg8cRTI9yHQ02IGiywCSnJRFahI3MbX13WNnSJS8FAUecDTeXcL+/DS0luP9pah72i7kdEB\nXiKnb5+xNuRzJUFpQJ6SRpI4CQ65cRwxZiQvMvJZiYqgOJqTxQVFGnzzAPfP70zROEsQEdt9Q9N2\nYGyw3B7PqMc9zoayehxH1osFRZ7jjCPPKyKlqHdN6GlGAdJz99495osll5tgMz06OqJp93gy2npP\nURTcvXtOkqRcXl5yfnLCYb8FEVHOVuEknCQslyvScoaaouKzSpCkgqHr0eOInAJZhYfddofVE/XK\nQ3uoubh8wXq9DFZVYLVY0raHKasrCoGKeKQKLQrnDHmWMZgQsd41Hf3ogmolcVxfXXOTbTg7O2e/\nq1ku1ywXR/TtnrLMePDKfa4ug8wupOUGhYDWhrIwjE6H5ASlGLTm+M4pTpvwA/4yeweB84IkyzEy\nQjuLHQyzqgxtj75lHAeUEERCIKcZwzAMt6YSAJUk6K4lz5Mpvl5idfghropyQnYG7WxUZmht0EYj\nhxDx3nZTYjYePfTESQxCcOiaSSqnkVGgyiXTgWI/SSKXq2PGfqAocrIsmXLvPM+fPWM+r7CjYXl8\nlyytGEfLZrdl17SAQMaCp8+e0tYtVal4/bVXOD89wuuBRIlA8osCj/lQh/sAaLUhjhP2Tctms+Py\n8or1aoNSCd53eOsD7MkxxQQFroOTgcO83+8ZTU+1OCXNKoxzYKdZTZ7QtC15kaCUoNMNSRqTZjEy\n9mTliqu95elljUw9B62Q+YL5nVcQWcTAjHbXML6E7BPhfEwAqkV42+F8kLkKb4OFGYcnKLOE82AN\n2nT/Rva5f6c3YedsgJ8XYXCx23RESYyKAq/BWujtQKiHHVI4vHMI74iER0RRgHHoMbh9lGKepWRK\n0XYDVmu6fsBahw08eJyXKCGRSuKFnKQ1gaLmCMYLJQQ4gSTCRY5YBXBOJINcKY4iTo+PQ2Jv25Jl\nCWmehjimVE0tE8E4dJix57DZAPDkyYdYa6jmc6QUt3bmm5sNp6enDIOmbTru3n1AkSc4o0O/cOi5\nutmBTNB2pKgqnJBcXFyh0ozl+ohxMJRFyQ09XdsQCYE1lr7TfOS11+j7PhhFrGEYehKZsFyt6XXP\nvTuvkmY5KlHMqhQhI+IExtHhtcGbAa9HDm1LEifgBWboMEZzONRUZcU4NCwXBUUes72+BAixQSLA\ncqJYhnK+qSeDQ+inZ2nGoMNQyVpLmuZY4/AuyJk2N1uWixVlWaLHAFUJQKAFRjvW6zV13dC2HZvt\nhtVyRZKk1PUBlUnm81mIv2pb6sMBbQwGgXeOsig4vXOHuq4pyhw9DESRwJiB+WLBZnNDnqVIG71s\nV+Ocn1JbwmY0jr+siY5ihXUBlxjHCVEicWMg2HmCI09GMuSrGccwdPgJpBQy9yyj7hmGAS/C6X6w\nnnEc6IeBi4sbVus1dROkacZo8jwAeLw1U8UiyNKE7W7L0dEavKfrGlQSE6cxw2TrL4s8DF73W/qm\nYVFV3Lt7ytHJnAhHEhfMyoyxD/fT9h27ug6oUJiAOikQBm1FUWJdcLh2XUeSxoGP/CtUU4GCpsnS\ndMqJi36ZBWwcGIHuNWOvMc5QzQo8niRSRCKi7Q50bUNZViyXR8g0ZyTh6XXNYBzPLq4wkWdZzTCD\nozOXzMoSKyRWgJNhyO+dRBKjZAoiCTm8LmTPOTOCV1ME0ldBxtyh7iiyMDho6jYoIaSk7QaGoQsI\nR9QERQkBftZYxIRQjJKENE2nnLiXfV9JpgQiS0jTIzabPdebLc73xFk5xdeHXrBzJgyd0oQRTxRL\nIi0JJFIPXoS4E2tQCrJUohTMZ6EEzpKCSEmGccAaw36/R0gfbKjes1ouiSMxpVxA3RyYzyuqqiCK\nE0briBONbkfSJKfvRkByfnbOdrNhvT4lz2bsNnuePn3OyZ27vPHWmzz84DG7uqWaLxitw1rP+uiY\nd9/7gCRVOKupqpzd1nK8XnH/7j2GYeCDDz7gwd1zTo6OuHz+LNzjakXT1OF7gkW1EKcZEDMrEoZ2\npB8HIu9RONr9jqEPG/16vSaaFeRlyp2zIz7/+c/zot6zXgfdeNe0VFXFwRyCYN8arDF4E/rrkgih\nACRDP6JUGCK2bYv3njzPb4eGw9S/LooCKSVVNSOOU66vb1iv12w271OWnkN9QIpQwvspGEB4eOWV\nV3n8+DFdE/5tM44cmgbrPX3fsVgscM4ydC3r9Yrt7hAGb6MmjiKkB6sNwzCitZ0eGp5sIuQF6liJ\nJQw0HeCFIE4TRKR4mTZtvUQ4Q56n00buGcepDz0Bp5JEYT00jWPQGmMseVZiDFxd3pDmYXgYq4wy\nL7BmZDYr8dYRRUHZUxY5282GMs/R44BKE4zwWGeYzwtWxwsOmy2xdDy4f0wiI7pmywvXkUYRwhua\nPCNNFXXT0I8GIRSzKaBARgnGeu6s1ty7/wpxkobwWW2QUpGk6fTQD1TCKIomEqKna3tAkmUFSiXE\nUUykFMII7GhpmobVejkZTELF4YwmSRR5cUQUK9586+MchoHLfcu/fPdhmHlYx6gHxjQjyYtgtFIR\nTgosHiECqiBY0cNhDuvAgIoiFI7RQSQUcaSI/FdBO2LQA4tFivVx8HnrEetsKLNVjPEAAkno4Tpt\nSeOYSIIXHu1AGIPzI5EM8djGCbR1RCoNVtnZDG00bT9JXrwlUZKiSMknt88I6OtNGOhYgzcB4B5O\nvsE2XeQpZZEipGG9KjADaKPp24a6DfQnIaCqZsF5Fcfh4bLf0XUBJnRycoLWQzgNFDlJkZHmnsNN\ny7Nnz5mVFd4bfvHtdyjzjPOzs1tWsVIJu31NlhbkVcnNF9/l+OSEmbG89/ARX/M1GW3X40bJgwf3\n2Wyugxnh7BxrLV9894us10vu3LnD5YtLuj4MLheLBQ8fPWJ/2DKbV7Rdzmq1ROQ5sZKs5zMiZ3nx\n4gVtXTP0wSUX4cjSGFXmtH3D2AteuX/OdrNDySDTsKNm7HqqvKDTY+BRlCVOByt11w/IOHAgmiYA\ncvrRcHp659Y+/OzZM+q6IctymqYlTTPmsyXOGYqiREx28bt3z+n7gf1+x+npCZvtDR8/fyvIvqLg\nbgwDqdDmuXN+xuXFJX3XUc3mVPMZ4hDs0sM4khd5MDU0NXlZUW82wQzkQ280KDgi7NT/7oaeru9Y\nlBmxiie+rcIJgfSONCuIvKcbepyzqFgGk5EQCBFhnUbFMgQYdB1xWiBkhJQBO9m0O5zzeC9CMkRe\nTGoIjxAO4X0oqZ0jy2KcEZR5wm5/M1UqPRcXz7m5uaScFQztgTSNWORHZLFC9z1WpXipaNsG4cNQ\ndxiGMOxMUqzzXN2EtGUVBbBRkuRARAjQTOjGjjRVt9FB1lmiSU1C5CnKkr4JVvDFYkE08S6sHsGC\nHjTSQ5amk9Ep5L8ZM2KMxnlJUzfMygrilH/x9rvocSAuE6SAOFJYPSWTW4OMBOAAN80rPHFsSJUm\nTzwkjtEGLELkPZqBSBrSyJPyVXASHowBEXz2R6sVbX3F1XVNmpeM1gcOrg3ZWd4bYiWJIj8NIDyo\nCOMd+YQmlFFA6+V5gfMSbwP8fVZWOHegt5Y0UsQqQjgfbNJKYLop4FIp8CbkSwGRkMgoZlbNqGYp\nWSIoqgqtO4yGrhtJ05goKnE+dJWyJJRaiUqRImLU+pcTAGREWlUIoajrjnIeo6KYo6M1U/o8zlqa\n+sDYd1xcXPD6q6+QZRlnd86Yr1bUzZ6h7XA29Dbv3j3n+c8+QwnPYl5R72rGoSWJFXdOTijzgsuL\nS64vN3zszTeQwrO5uSKKYqpqzr179+j7juubK9IkIU8UWRyxms8oJ9dXKyRZnNB6WC0WId4pTWkO\nNSd3Tmn7Bq1HokixXC4xU8maJCEp2xiDn05/3gXdtVKKWTWj145+cEQyxdnwoDLGkOc5xhju3LnD\n9fU1y+WSNE25ublBDx1JLFmv14yjYbFYBUsunqurK95//70J/m5o2xBhU5Yls9kCEcXEqaGcLehH\nE+y2bYuIFEiJUsmUUGzo+44kyxnGkSwvApfYQeQDGLxpA9kLYDZf0rU1zaDJCWQ5Y4MEsigCt8Th\nA+hHwjB2t448CGYnrfUtJyNE7ySBs6tC2rc2hqLIEVPYpTGGNIkAx3Z3Q5nnxFlKHCm0cORFRpbG\ngW1sBh49ep9oCnL1zpHFcUAGSIlMFW2nybMc6R1deyBO0mDF3uwYuxEZpyzXYRDpUIx2pOk0SAVS\n4QSkRYExBgGMLxOkUxlmJ0OHlHOUUkQyDg65QiCFwhI2667pwkNIRlhnb99XFYXYLu89aRJzfHTE\n7tFjdtfXRMJT5Qneaco8J40VFy8uUMJSpBEicqjpPiIJ3a5jvk45OsrojaWcr+majpP1mnd+4Zqy\nUlRlSh59FaAsrbMYZ0hUxGw2YzYbeP58R1oorHfo0ZHIKGgNkdh+QIg0EK2kxBLKwJtDSyQEmYoQ\nSqIt1E2wYwY9YkQcS4QCKTxVEd8GaWqjMTZE03gdAgIDX1iglCSNY8o8o0gUKg6UH6s1h12DiCKU\nCifGSIWsOQAzWoQfMVqgoow8eelBlzgrabuG45NTDoeW+WJBWQaI+LOnH9Ic9lPJJ3ny5ENmRU6W\nxnR9zVF8wnq55Iuff4fmsCNLHrCaVVRlHmKbEkUDPH36hNlszunpKfx/5L3Jr6RZfp73nOmbY7hx\nx5yrsrqqeqgexHaDkqCmYIECbBiWd7I2WnJFaOOFoIUgQIAAART0H2hP0jYMe28TsCibEiXabInN\nHmrKoTLz5h1j+uYzeHG+vBS0kQURUgMMIIHcZGZk3IgT5/zO+z4PcH5+wTe/+TGPHz+kbva0Xcs4\nCObLJbPZjK7r8S5Q7/acHB3T7Bv86BizFGst5+fn1HXN24u3pGmsEAshePDgAVdXV1TzOcqYiLS0\nNcO71yF4NvsdaZrGKGDb3iUnxtFxsz7Hi5RifsDR0THWwc3NmpOTE5Ikpeu2gCTPCzabLUoZ2rZl\nXlU0dcvQX/Do8QOstSyXC/q+wxhDmiY0TcvLly95/vw53//+92Nky5i4cNuRuq45Ojlmu9lQ7/Zc\nXFxSFDnLgxVJotisb7HO431PnuWTNxwGcQgAACAASURBVGOcCkEJdV1zenp2NxMOAcQUZXQIhNLU\nTUee5+zr9i6zPAwj3sdLZv4tmaTWGjuOk6JLMQ7dFOyxCBEvmIQ0GBP5wFIq9LQTl95FsI0WlHka\n4UomQxDt32WW89N/869RQnByeABSxNcjS5FC0jUtAkiS+Gf7fqBpWt6OF9H1JzVjN5BqRTO8u8eI\n1eOqmnN0dBIjhyFGR6WUjMN4x9joQrx4TtKoKtvv6ylquYzwImTcxRpNkefxvkcQL9FlwKSato6X\n28vlEh8C8+trNjdX6DAyzzWJ6Ll38oAQAs9fvKDSmjGMZHIkz6Jq6ZNPPuFwtUB3tzx+/Iir2y15\nOWc2X4IX1HXNk+MlHz59SlZU7Lo/A+MI66IRVYpAmuioDxeS7b5BmoQkMbGEIBXOjSQmAxkv1Ebv\nGftpx6VzlNYMQuKsZexrxNTq15OTzpgEx4gUAmMcyihQmt4GXGcZ6gY/WlKjEVJHYLxSpIkmeIcU\nhtRo+qGjaxukFCRTnz5yjWVsBknFOM0wQcYRyNS3z/MKozXa9NR1S29h7XekSlIUCScnR6wTycFi\nzre++TF2GNjc3iBlwWo5pyoSjAItPH5wnB0fxYRDlrDfrpE4Ei2oqkPatuXq4pKj1SG2H3j04AHz\nWYUUscigteD07IQ0TVktVwQXmM/j/HkcR4qTgmfPXtI2Nc67eAHWdlxeXZJnEaLzs88+wztYHR5z\nenbG+++/hzEpfR8JblIr2qahHzrSLKXMi3ikHxxN00VOQZ7TtT2d7RDSEAj86Ec/isbiCS05DAOz\n2Yw0zSjLkq7vyUzCMPS8eP5yugQKzGYVZ2dnrFYHfPrpp9S7ljTJ+PnPPuXs3hlFNePV6zekWcrq\n8HCC70j6umNf71hlGUpA27ZYF3kmbdvg24aDcoYdR4R/p92JzGk1lZucjyB/H6KPcHQ2XtQFjzbv\nPoYe50aYDMDxV3yfKmWA8c6fOI52amnGsogPnoAk4DFGkaYxCdHsd7TtjiQxaCnIspQizxmGDiVj\nUkgrTaIUt9fXWGcxWRo3GkKxWW9p9jWJni7tfFSLzecLxn7AOYHRKRf1BpXNaeqYEzYmY7SOPC+Q\nSqO0mWQGcS7nnMeolCQBJUPcBImAsxaldGwLoiYwpYy250k6GoUMI0JBEB5nLXIajVxdvo1fuvM5\nv/IXfplvfP1rXG/XlIsFZV6QqIS3lxccHR3F0klRYK2jGzseP35MohXSNoQApwfHpPmMrhtBSN4/\necDXHzyOCRUbkJMr8T/28Qu9CDsfd0XReBrJUMdHx5w9fkozxmZZt496dmR8w7d9E3epKmpK0iyh\nsR4dwLke4R226zFaUaQJWWJwdoyMXjUpk5g2ZAIG54ilSUmeZkAApQleELwDFFmaTQqkgB0sIkiy\nTBNtKvHNY60FEeWg/WgJTMexIFmUEeDjnZgUM5qm7ji+d0bXD8yKhK5rCaEgyzSPH97n9PSYLDV8\n9rPYuEp0QiIFXb0lNYpZqTBSULc1Y9eRJYaz4yOCl6RJxnw2QynNMEWpjo+OOD46QghLUWbs95bj\n4yPKsuL+/fuURYlWmqZt+fTlZ2zXNWdHc46PjiirCpMmvP9hxEq64HEE3rw55w9/9CO+Or/mZt0w\njDCbl8wXMeR+cHhIUeVcX11xefmW+XyOlgoxyReLoiSvFry9WvPq/BqdpKRFetdQapqGNE0py5KL\niwt+/vOfc//+feZVSWo0i8WcCJ0Z2GzWkxXCkecZ7733hPXNjvnigK7reP3qDavjkbws2DcN8+WS\n0ft4Z3DQ44OLcbMQj9Bap3gfd6baGJjEs4qpmLFc3s2tIcYgx3EkydJYgOgCRsfXPwAohXRMBZG4\n0P6JrUswDnaa/cePbOUF/Rir0T4ALi5uWiuEh3Hs8T7iJ5PEMJ/PSIxmHHpCllCWBYvZjP1uy/X1\nFbMkYzct+MIHvHO0bY9UCfNFhhKSYb2mqhKaZh/xp/M5t7cb8tkMbTLeXl6hk0hRS7McKSVlNScE\nMY0NBNLFTY8yBj+134Rmos4phFK0dYcxGUoZvIhJGTcp6PfbHUWZ0XcdSRbJb9aNjLZHK41W8e8o\n5ilFrnjy+Izb7RpTpEgHiUx47/SQJE3ohj7O7AVoE6vT7a7GqDjDVmmOHQX3Ds6icHi0pEmFt44s\nEejiz8AiLIFgY9Qsz0q+9c0n/PJfPGPfB5599YbVYcdnP/+Um+sLjElo+4HgA9bHjLGUHmUMWmeA\npB9aZLA47wnWMZ+VzGYVQ9+iNNO3vMLohN2uYRgHRucJGLIsZ9f0aAVCSWxwDM7iRolzsO8astxg\nEsPB6oCuHei6Di11JLW5aJgdrIs/xDQn6BHvBe3E9j2/uObw8GCaD4JzgXrf4GyH8wO7zYa+bTg+\nWnJ9dUmSaFYHC7ztGLqek+Mjqqri9vqa25srEgXZvMQNHQdVRRgtwnuqMuPw8JjRerbbNcvlnCTR\nSBEosozToyOE31CkGUWa8uTxI9yDh1xf3/B//O+/g9aa73//+7hxz3Z9E78g04Sr62uarmO2WLDZ\nbvjwo48YfOD0uuaP//iP+b3f/30ePXrAd7/3XQCaifJlg2C+PKQqon03jg1SfBCRyyw1xqQ4JGUV\nbcHGGKRS7Ouam9vbuIBPu+LRpXTNjhAc+/2OBw/vY97tNoW/23k/fvwQpQw3tzfs9hu2m9uoetKS\nV69eAjAcHFIkOWVR8uL5c5JEkyaKPEtJswxBuKtDmzTBWov3HpMmjPv6jhB4enrK7e1tHIcNMdPb\ntk0senhBQNL0UZWkpMGN8WLvXfQtyzKEUHT9QNO2EaSuJV5GLGpAkCYZBwcHdH0fyXCpYZRR4V6W\nBTJA3/XIxZLMZHEO241oqRjsgDKKtutp9sMkMvCAwg5jfH3TEmEAqbm4vKEqSuqmZtsN7JoWZVJu\n1zsAbm+vOTu7jzFJHEMN9q7uHpuCGmMyunYfDTlprOBHO048mULEB0gVP0MhOHrb4vcDznfMREWS\nGFRqQMaCTJHntG2L846+7ShVwaKK0tsiLVBBkhUFu/2WssiRSkVEqh9pu+gUdE6SpCVZWhKEptn3\npEmB9DD0PXme03UN6R0j7z/u8Qu9CP/gO79EkXoOD+eUZUkQhvU6Ap+NqpBSUR2ccLNraPs2Nuq8\noxsleEFiEpQqCN4TwohRmr7rWcznVGXGrEhxwSGSOM4YBotKFN1o2Q0OhMFKCVrjhhZhIlOgLCp2\nu2a6CIBd3aKkwLYW2Qe2+wgIyrMCH+JFX1lGaWboW/K0oO8GRhdh4mOIc8O0yEEZetuQ5jmz2Yyb\n21u29UDfNXjrI65ws8c2LUVmqPKUXCmOHhzz4s05946P+PjDD8gUpEqx2e345gfvsbu+pMxL7h2t\nQAlmZcLoRsrZirLIMdrTN3uWVcl3v/EN+vc8hRQkBOqh5fWbCy6vL1kcVsyWC15fvWKWBmYzQ5YJ\nnO9pmi1IRZ6nzJePsdbx9Y8+5up2S9Pu+eorwXqzviPw/cvf/xFVVdK2LfdOHnB5foU0gbysWG9v\nKao5b9fXXNxuEbpgOZ9zs9sQxa6GPM9ZHB4hiAaYrm0pyhllVSHnGfV+h0wEn37xc7IsoX3RsljM\noyVEwebmkoPFAcE1fPeTD+nGkbcXlxiTUCSKum559fI5foBhjHjJPEvwznN7c8tqtYyYVuHZ1y3e\nuQlgpKnrmiAV2bRbury+wah4rNYqVm+T2YJhHOntiPOewXra1uPGuNtLlYkONWOouzayG0Kg6Vqy\nImMxWzL4gJz42EkayyHb9Z7NOv57dnLdDW3PyfEJwQeuzq/5YvslfdfhrOP4aEUoNDvvSeczUqF5\n9eotSguaeqBvR8x1g04lg2s4WCwxZYJD0PYtBoWUKV3v0BNPOKsyTGooqhkeST961OhAEZGvFrSw\n5FmK0pbgxymO6FCpwGQGaTRapNjRo5XgfH3O4rAgz1KyPJkyxPGknGVFLGg5j05zrBvJqwOCFHhr\nyZMUKTRKKoaxRemcoXfkecwDex9IjSF4SZJXSJUQZIoQmixP46YuSdAaejegMoPQ/xl4wv+pH9/5\n5LuM/QZjBMPQUTcdTTMy9C6qqqWkquZok1DvtiQahI+RnqKcYXQkUJ2fn5MYg5qqnO8USFFR1MVd\noIRd05IHiUkypNQokxJsoGl7hjHuvobWU/cD/egjZ9X/CRRGiAgHSjMNwePaGj8xcTOXRedbGelj\naZEhVFQeWRuD/V4oLm8iWyFJU9abHYeHx9xubqj3e8bBYrsa6S0fPn3COI50IkDw2M2WYr5iu93i\ns5Tjo2MIgbrtMFpTTjf/4+jRaRaBRy5M5uCGm+trlrOKRCc8uv+QoXfU+y1fvXrJxdUlSZZRFCn3\nzo7px4Es1Qz9lqEd6YaOJMlIs4SinJFmGVLFGFK0nEjunZ6ynLjA7/x+jx8+oigKtNZsNjVOOOzo\naW5aglB0faBpo0pK4NCp4p3i9h24Zb+L2p6yLMmTKVsbPFIEpBQcLA7QErquwduei/NXBNujlcD1\nA36MRQchJNVsxslqya6u6eodR6sVAkmez1jM55R5xqtXr7i+vmTjLOcXbymKbKJ+9SR5FqFIJsGH\nlmHsOT6OEPUkK7Bjj/SgtSF4i7We4CUCRd+PdF3PZrND6Gjo9tNMuG86VqsVIQSGcSTzUBYF2hi8\n87TdwGa3oxtG6t2Ose9IjCJRhsVixmIRY5GSmC54/fo1282WzW3N8fGKG7kldRlCGvZ1T/ADQUjW\n621UFo3xdKBFwuHhimCjnrhrepROCWikEjjbo6cdbF6WrI6PKGdx1GbtiB0ERmsybciMRIdACDay\nTdL4GWzaFpNkEwEugtyNEVxfbyjyitk8jxwP+66YI6P9uB+nNEn8LI6Di8aQqd2qpImeQeumU3J8\nL8WUisPagDEpZTkHUxCIo8MQJEIJvPMEwKTpJAOOF/p/Go9f6EXYOUHbjFAk1PVIXfcMQ4g6lAkh\nmWQpJycn7LcbxrElTxSjdfQ9jEOcx7kgqNuWRGvKImWz3+P8SKL1hN9LkNLjnAAUWhus7ehthw/v\nfhiCvutxzmBD1A25ENtNzjLJBEdG72n6Hu8HsixFSUXQgjDGplSmYvHAe09RLiAIdBYD7rPFiny0\nZHmGkILeedIiJy9n7HZ78AHUQEBQVnNwA1dv38RLS5Wx3WzI1Ir56ZJXL1+C98zmS9TFDbe3GxbL\nVaTL5RlSKUqTsVjMub0d+fLLLzk9PmRRVdze3vLFp1+AlHztw6/x3pNHvH7zhvV6S1ZkCGdpdhsY\nW/I88gg8EasY8Pzu7/6fnJye8f7Tp3GUlBiOVwfsdprD5YI0jbul//JXfkgIIaIkH3jqbuDt+Vu+\nfPYCITU6pFSzGYOF2WyGSQzz5ZL79+5PCno7YTDhxz/+MefnFzx9+pT9fsfp0Zx0saDvWtY31xij\nmBcFShY8fnifPEs5XCxomobPP/+ck7P7pGlMfh7aBVJoTk6PcT5Q1zVtcwOhZL4oUPqIg9WCFy9e\n8OXzF5RlxZP3n4KQrLd7GCzWw9m9R3dCg3K2QITA2NfUdY3SCVoE9vWW/X7POMZImXWWcXCMky4r\n+MBut+eLL1+ijWY2m1GUOf0w0tU76qaLOi2lUVozX8y5d/IBqVHYoaOqEoa+4/bmauJzQJZmhDJQ\n7xqs9bTdSL5a0uwa7OjQ2nDx9obDwwMODpY0zZ5hbCiERnjH7e2a1XJF0w84KxDeM4ZA11tM3M7y\n/tc+5sH9R7EFKCVFZphVKUWm0YrIS/Eeo0WsqQfPMDis82R5ilAGoRL6waOIO97FfIXz3YSljaOf\nvou6MGPSO0tOCAIz7chjVjmLxZq2me5x3s2AFTe3G0KAvJjFy0+psePkmPRMf7fB2oD3juDllEt2\nGPNnICfsHHgv6TuLdxKlUoZ+T9tavI/HvmHsWC4POD4+5uL8FUIqnHU4DVJqCJFL8M6gjJgSC8gp\n5aDxSMbRMXqouxEvbDQUmAQlEyA2sAKS3VVD77vpYk7e/dDF1Dv3zsZmkjaMXtEPjrpr0GZAG8Ng\nI9tCIOh9jQ+QTOmIy5tbZlVFkhfRCL3Z0nXXJIkkLypEUdBohcKx28fgfzVbopVgt9tj0pIvnj0n\nMYanH3xI3dScv3rDk/ee8kc//nFUzaTJVHYBoTVtU4MPPLh3j5PjE8a+5/mXX7CYLzidxI9vn11i\ng0Mj2FxfIo0mT3P2Y0eSxi+ierfhZr1ltjjg/v37nN67H43U44icjMNCCBaLxR0cJ01TnHOcnp7y\n6VfPefHqOV0fePL+R+x3LdYFtttbjg5XeAYILcFmfPbznwKCk9MzjDYUec73vvc9Xr58xevXrxEM\nuKHm5PiIxWLOfluipKAsMpaLOanRaAF4S1fvOD5YcP/0iMViQdP2U6MRur6Pl7upwDmBFBaTGbJs\nSdcNzBffxtrA1dU1n37xjPv373N0HPkfJskxWSzIAFHCmWXM5iu8E+z3UY3UNh1DPyIQkYuiE6pF\ngSMwutjwnC8POLkXSx7DEMli1sYT1nE1Qyd5LLkIwaysODxYMA4dXbPHDQ1Kek6OV8zKBW07cHlx\ny9X1LfPFCikNUiZ8+eItWke10dHREdYFbq6vuLm95vjkAGtj7Xm73aKUjuOXIOnGETd6TJZTLTKq\nKu58n7z3NU6PjtCJwdqBxGQkSkzSXo8QPiJetQIZIufFembzA9J0jjIZUmfY/p3x3EXjxmpB0+yn\nWbwkSXKyLGWz2yAn2W9khUeiotYxS+2cZ+gHgu/ohx7fBZRRk+UkJ0kynFc4LzFZgVaGrrN03Ug/\nOMqyIniHdT3WAkHBn4WZcAgCrVK8G1EyQWvPbDanGfd0Y48QYlIXKQ5Wh9xcXcbbVgkECUFiXSCg\nMElKW+/Z1T3LecVoLYmR2NGijUDIBGkKRuujjVUZpDB4H5tvCImfFlcmv59UEu+IzjDvkRKE0Agh\nsRa8nz68UhJQccd8l/gA5X18rkMcR+gkpRst5xcX5HnB6vAI5yxD35KVM/I0ocgyXN9SzRcIAbPF\nAUWesnv2nP1+T1UWfPHlC9abPR9+8AGLgyM+//xzRmtpmwZEQChBUZbo1FC3LRDIs5x231CVBcvF\nivv3HvDq9WvevH1DmiUEEVkeqTH0Y09jLWVZcHJ2j7btaPst9+7f5+jkHkHEXPRuv+fq+pY8qyiq\nirwoUFpjpyxwN8adb9M0fPrVM1Sacf/hGdvbHiE0Q7fn/r17uDH65oo8Jc0Mh8s5+6bj+vKc7a4m\nywpOT+/x5MljTk9Pef3VMy4vL5lPTrjVwSH7/YbEGLI0ZehqHCDdiJLw6NEDmnpHvd+xXB4wdCM+\nBPb7GqkFLkQAk3eWXM/I8yLuttKCths4kor54SGXl5fsr68RQlDlBdY5Eh1hRdV8zn63Y7fbsb6+\n5ejoECEk280WZ93dcxNC4KUkclVjsqNpW27Xax4+fMhiSl0kWlGVKVmWI1WCF4LRxuSGVBpjDDvr\n2G02GA1Gp9RNS113KJ0w2DiKqvcd7dhS25H5wYLRR1iQkIosjyclgWVvO7TOcA7yIme9rrFjYEQy\nWxzw+Ml7rA5XHB4fAvD40RNSozBakE9RTikCAh/VYdNlbsBHQzkCoQxpUiFlSgiKYfRRINrt2e9q\nTk5XBC+YzeYxJmhjDbvrBpIkvQPtv0ukwCQY6C3eDzEBNS3Qzo3x5Du1TaMSSmJMzmj9XVMxatRi\nWmnCYCOlRiqFVumfyjr3i70Iez/NffSUBzYxFB88zsUGltIaERxplqFMih16UqXpBovJkwiGFikI\njQuC0QlGD5lOcCHi/QISZRKM6elcj3VxRBGIEjtjFOPoSExKVUlQI10/4l0AEUkSYpIqCvmOCGUQ\naJQM8ZvYSrwQtG0zCSE1q6MFRyeru51h+2/JD6sqAq/fGTtCcFRVyr2HB2g8ZWpIleD5s8/54otn\nOGuZzWZ4D13bYd0NX72KyMvRWrIs+uxMEueM2ki0iFnmvuuop8rxrJxxu95SLQ74/MtndH3LbFaR\nJJq62ZHmOYvFPKruheD6+pZ93fDe+09Zro5wIUb4mqbl9eu3/LP/6/d49Pg9Tk/OWC6W3Lt3Rj1R\n4/757/8riqLkyeMnPNaSP/rJz7i5aTAqY1ZUqFlG32ypCslsdkBRpDRdLB/4sWde5dw7PeH6Zssf\n/Kt/yb37D/j469/k3oOHtO2em9sNR0fHdF1gv2s4mM/o25b9bhtZv0KQZgltG1tYXdehtUEpRZJm\nFHmGdSPeCpp9Td8PPCpmsa3nBf34zuasCVN7z1rLm9evaZI98/mcehfn3z/76U9jtlkqtusNgx24\nubrCaDPZODxFUSIFbIaeoog7yu12S57n1HXN8+fP7wzJjx8+pCwLymIGSvHZl884Pz9nsVjgjo+Z\nlTmJyagWK05PVvTdyNXVDU1nubzekJQVpqhw7YDzgaYd2NbnBKIIwBiJG0aC98xmFc6NpHnB67dr\ndq1lsThis2159OAJ3/ve93j06AHaxIgcQGI0RZ6QGEmiBMYIlouKMs8JNiJfvYipkCAizzkzCXk2\nwweD8xKJZBx6tpsNTCkUqeQdd/pdU9DHmmJcM0K48/65ySn5LjUjgsW7cfq8iuleKVqs0QnCSzxy\nggfFnfm7QkySRMO4UjJuSP4dk8Z/zOMXehGWSk2AND8RzhwmUZycHrPvGrb7HUGlcddStzRNi/AO\nLwUKTyMGvIsMjoBCyIR+7Fmv98yqHNkNHMwrbGcJk7HYeUHbR6231skdRNy7mAfNUn1n4PCeaN/w\nHmsjgSxmIBP6gfjNO10exJNLoChStDZorem7ls8++xMX15s3ryimWudut5kEkgFkNA0YBbMiY16k\nnKwOuHdywNe+9jH1bkOz33F9fUXwMPrA5uYW4QXn528j6SsE8iwjRSEmXGCWpyijqIoCbx3jMPLi\n+XO+/PJL3lzd8OLFS/I8wwWBx7JaLSYgjeAnP/kJLkhO793j9PQBWTZjGBwXl9e8eP2G7W6P1obl\nwRGXNxsurtbUdc2HH354V0748Buf4Kzlxz/9OZ13HB2fsZzP2W93VFkGzoI3JBrSRGGHgb7pGeg4\nmM15e3HFOIw8efweH3zwNf7g//0Rf/RH/4Y0zcjyOS7A4ASbTY02CVlR0ta7yOGYVVRlRZJGbORm\nt4sV55UnK0pMktD0I6CwvccOgbruefXqVby8EpqLy+uYJx5dxHM6S54aPnj/CQrJwTJmkAEI8RSx\nb2qyIh75JYGqKLFjT3AWN9q4CC5mNONImiY8ffIoqr3aWWQojwNtveNnP/0JeVaQZgV117HZ1kit\nSTLHerOnq+OF8zAKvnp9TZ5XbPYDu31H3Q0Mo+fN5S3j6OgGSzc68iJBinh/UomU5XLJ7W0cBxV5\nye22pu4deVFyta45u/eYX/rBn+dw0l9lScQGAFRZRp4aEgNGC+azktHZyDdJMpK8orERz6lFQiI0\n3oGSGba34AJaK7x13Nxcc3IazS/eWcTUugshsjJciMD/aMqJdy/eW/w0/5VSRvrd4OJp1Xu8G5E6\ni5ZunWNdvINSWmJdB9MJW8o4G266eIpESqSMzT/Bn4GLuYg0fKftDgTiCEAEx/Hxit04EnRG33aT\nbucdkd8hlWQYPN4N2OBAKqwHHxT96JB1z8Fixq5uSdMktriEmnr3fjreSOzoCV4gpbjr7duxgRAo\nsiI6vLIcCAy2xXmL0YZ2CDH/GeIlQJImEQ4/jKzXG+pdjzSK4P3dRVWRpbE8ouTdN70PYL3Eqhgh\n22/WnLuRr1LDveNDzk4OSYzCCJiXFSYrKOdLzi+usKPDWMfLr15wdnSEkdFUkaUJeZaQpibqopoW\nP1o+fXPJ1eUlL756RTpvUCbFZCVpntP2Lde3W7589iWrVURHPrz/mLOzB+z2O/74j3/K28trDlZH\nHJ+cMZsNVNWMNJ/x5irCbT76+ic8/eApB6sVEDXt1loePnnK7W5L27V88dlnLOcFna8hjFRZwtgO\n9DvHMIzsB4vRKXmec+/eKdc3G376kz+mmq/46KOPePXqDV88e4bQiqoqefn6HNzA2ckZ2qQM4y2D\nDaw3W1arFXNiTXexXJFmOUJpTJrjAaSkbztSUzCvAtttQ9uMzOaCcj5D327YbLeM1rFcnFHvdiyq\ngqHvKfIiVtzNNA8P7+aghqLIIySnzUkSRZbljG1LY3vsaJGJYVYUzBdzxnEkNYrUlOSZoes7pHCE\nIKnrgYvLNYMNqCQBa9ltW9pdi7dDhOWLGK8L3LDebGjajm4YJ2NM4Pj4GN11+N0enRiWizlaBJzt\nGcaRoii5vLzi/tkJu8ZhijlOJPzSL/+A7377l+LopcpRWFKtKbO4MM3LnDzTaE20oktITRFbcO82\nOlkRSyoiQYlojbGjwA0CCWy2a/b7HdWsxCSKssoRIkwtwUg6U8pgTEYQlq7rppSSmApeasptW5yz\nEeSjPMMYI6EmMQipIhNETTQ7FImaSGp+srMPsfCiVPz/CBkmRZf8U1nnfqEXYSnuCkPxWC5iocIH\nS1ll3L93xrpx2GGMUJwkjzvhcYxznyn65YD+Lgg/XaIJQTeMCOIPzSQpowvTblZgTBJh7N6hdRJV\nSkLglSOQUe8bvO0o8pI8UxRFDhRY1yO1oht8vPiTkjSNoBqtI66vKKupK19QVnPm8wUAP/xLfxFr\nLU3TTE2nlCTNOTx7hEDgxo6u3tE3e4wMJFLQ7taE4FktZhytluybnrxakBbzWDnNMvbbNVUVVfUn\n8wUyUQgZu/d2HLHDiJ9u5kEyny25aQaqWUmQAqFTfN/hAxyfnsWWWjXj6uqGzz97ztX1NU/ee8oP\nfvDnWa6OeP7VVxysFiAiz+Dp1z7k2598G+8d1WzO7/3e/w3AZ18+QwjJ1eVlBPIbHSN92jNf5Dw8\nvY+Rku3Nlm7fc327xgjHbrfmYLmViQAAIABJREFUy+fPeO/p13jv/Q85v7xmu2tx1vKd73zCwdEx\nv/vP/zn9YGnrmnmZsZhVvB06hBec3rvP2zdf0fYDo4s13CTN4o28iLbKsbfkecV+25BlBYnxSJmS\nZWW8axgty+UBz796yW63Q7qOxazEKB0jCN7Sjz279QYAEz/rKBMvj0KwLA+WuGkXHLSiLHNs35MU\nBbe7LUNXTwLM4U63tVqtWM5K1tuao6Mln33+JbtmjQ6KbuxiI9LEG/y26eIoqiix1jI6QVbOySqJ\nUJLejpgsI2iJydMIfvIOi0dJSdeP5EbSjCMXl1f0o2O+POLP/4Uf8uDhewz9yMFsHtMObkR6SzZF\n1MpUkxiNEA6l4kZkdI7gA1laUBQz9t4ShMQ6hXMRdD+0A360+GFkfbvGestsucAk8TMWQqQpChki\n9QyP957B9VgXxxRyupSLZbsp1UCcH2sdcO2ITjJMEk+kHkUICqkNIUjGvp7kvQnGJFNVPG4EnY9l\nK4UC86ezzv1CL8IxMBb78V4qpJKo4PFEzGGVakySUUiP9j3SRVXNdrNFCD9RnjRpsZj6/lGPghS0\nY2AIMaZmhSAVimDHeLH2znFlHd5ZskJjTMydWhKMBq1jrlPIgLUtUiVAuHOKHS2WNE2HUoK+abAh\n0A49ZVVihIfRcPn69cQwBf4H6Le3lGXF6B379ZomBEbn2G+uESICuY1SaC0xeUGep5RFzsuXL/j5\n89f0IeHg4ACpM4o8kCrBy/0tVZ5juwGRalItSLTCjz3tOMSdQrA0fceuqXECylnJxc0LRmkRmWF9\nfYXSgjRLOTo6gcn1JnE8fHDGN779bZJqgVc5X61b3mwG3FXNk8cP+a//m7/Gi5cv+cM/+ANePvuc\nq6sLJmgchXbsmxqlHENbMy8XrBYrjpYpJ6s5OQHbtxyUCWY54+R4Tustz569JEsUXz37Ai0N73/t\nY6r5klfnb8B33Dtd8b1vf0Jdt9T72Eq7uGmw3Y7VokCGATs63ry9pZqtOD45xAuPVPHUM449PgSk\n1zgEF9st42ipB4ttOgZ/yyx4tImFkYhlhHEMd9yQtu0Zxj7ygwGdqIkfYhAhoLUAGZAhEAjks5zg\nDF2r6Hy8CLy52VGWEUkJnuXygOVyHufo75d8+vlL3nt6n/Iy4fX5JVmmUYnACUkQhn4cUTrOO/O8\nwDd7xmEgMQlZkrLf7tjebGJKpe/IsxRhPaMbuN3uyMuUvfe89+QJaZ5SGsF3vvkDTlbHuLZhVmSk\nckDjKArJrEgpqshPVolEaIFUcXwVyMiLKqYVXKDvAylRSwZgRzuR1HrqbbRJa22YH5TkpY40wzAi\niM8vfsGLO4B+kDDiyZJsku66iLgVHm1UvDwXnnb0CJWRZnPSdIExJc4Lxmm3HE+9KsYVvcQ5O3UB\nzDQeVCRGoqTE/llAWYp42RvHBMQivUSQGYMSgsQHRmEoNVSp4sHJIc45dvtdZIUKiZKatg/UTctm\nfcvt+pa2rSP6MjERQCMCWZFhBxfTC8KRZTnOe5IkARHi0d15vBVolZNnAud2eOcxeYFSsZKpVOyg\n93WH7Xs6O1IUBb4fSbUhDA6SwOHhim9+/M0Iipl4AP/VX/2rVNUc5yz1vmEYR/b7PV+9fokxCVfX\nV+x223iTi+d2s40shNmCbWP51z/9gq89fcqD+4ZZVbG9qskSTWIUwY1oEbnLepo1N32HdYF+iIza\nzW6PloayLPnwvcfM54t4bPWOxXKJUBJnPYfHR2R5hu0bTJIxioTWRhnn6CW//Jf+Mldv33B7+Zb/\n8bd/m65pqPcbgm1Z5oo8n3ZLiUBi6FuHU5ZZFjg7LDlcVEg3sFmv0VpHUhcWrTyz3PDRh4+5vNoj\nVcKzZ1+gtOFb3/kex6slUkuMMDw4O+P6Zs03vv4Ji8WKq8tz/vBf/R4npw9o99fUuw1pVtJ0ls2u\nxiSKVTFnu60Z7MhicYgdI+e3HvaYoqAeenrv8EKSWcfz5y9I0pQsy2jqlkTHmWoI4P3IYr6YQE3x\n6CuVZt8NVHnOOFqyRFMtUhSBse8Z+6iT79qooi+KhOViRlVVJMaQ5wVCwji0pIni8aNj3l5ccHz0\nlNXRgvO3V3Sjpx8HtEmpijlD16ONpu27qI8KEZJzeXERWQtCsb5aU+YJY9fThXgyUFqDiCYYleYc\nHJ/w/uqMk9OHkcnhR7SEfEo9zKuCWZVTTgbqolqglUEpE+9XlCF4Rd8HrHUID0lwDP3AMFnIm7Ym\nhChtPZgvMMYgjCUtVMRbKhHLRklsEr7LVfd2BCUxeRbJdsGTpxlNU+NdvFyUUpLlZWRniIA2BQGF\ntdMSE0AGhwshJiQGyeDGu/soYyRSKryDvhsRYiTL/xOlI9brNX/7b/9tXr58SZIkPHnyhL//9/8+\nBwcHfP3rX+fjjz++m8H8xm/8Bh9++CEAv/M7v8M/+kf/CO893/rWt/iH//Af3s0+//8+AjFmFn9P\n1NpMmiEpJEYJEm0weBIZ57ZCSLpuPtU9NVJqrq62hMMlw+khddvQdA3jGGHe2+2Gy8tLrq5uUATm\nVYmUhl3TkiQ51geqqkJIQVvvsD623KRIEXSAoW0sWRp9ZFL+CXbP2pHZ7IB79+7xg//ilycPXcJ8\nPuf09IysyKY6bBybfPzRt2jbFm0SEpMgVeQN2xC7++v1ms1mg5SSzz77jO12y+3tLfv9nqyYcbtt\n+NEf/REvX874zjc/Yn97RZolZKnCjY4kldHrRfS+jaNntJ5xsPTtgOtH0iyhygsWRUFeFhRFxc36\nlmEcOT485fGT92i6luvra2azitt9TbEo8N5zenZMEIr/9X/6LVItcUPDq+fP4ignONzYUuYJZRbP\ncW7oSbSmylOMsJweHVDlKZuba/ADwUZ+7nqzIa9iHdm1PXlZcXZWsjw4Y/Xmgroe6LqWNM/Z7Xek\nRcrZvQcgEo6Ojjk8POa9x48QfuDi/Dnf+vgpTbNnvak5PJKkWYlzA7e3e/q+ZxgsQmQoafBBxEJD\nuyfPS6SQtG3P9fUtWVbinEVpTd209F2PHQfKImd5cMDFxTWzqTH2wdOvE4Ti82cveP78BbOy5MGD\nU0IWOD08YDeuSeImEmE0UhEXmG5PLxyLkxMU46QwkHTNFnTC2fGKICQBS1lmnL+9pm47dJJS5hU3\nN1Et761DKCLwxjqEjIbmzbqmKDKGyflmnUOZeB/R9XYyXgtGG6iqOWWeoEW8f0mMYnWwZFZkHCzn\nlEVOWURzeG6WgEKKSIQLU8FqHOMiHLzl6uaSrmsRQjCblTx4dI9qMnNAiGQ4GcE+IYTJiu7vbCrv\nmB1pltLbOH4p84JEJ4x2JE1TvDdoKUmSBElgt93hg0Urg3fEnZ6QBCGA+DqOY2zj6SleKKamndYx\n+ielvBtb/mk8/r2LsBCCX/u1X+MHP/gBAL/xG7/BP/7H/5h/8A/+AUIIfvu3f/tO4fLu0TQNf+/v\n/T1+8zd/k0ePHvF3/+7f5Z/8k3/Cr//6r/8HP8EQRNz0C4mUUSeEj7Ph4C0yeHItyXWClHEQL52g\nGUeEdVjXcroqcUHEN/WYM7oDhJCstztOTk85OT3j1cuX3F5eMvhAqiKIR4hANVvQjTbyH2ZLrq5q\npBCMdogh9TwlTVOyJM7d3PTN/PD9h3zjm9/gyy+e89f/+n9PmmY0dUeel1PBI0LftUnvPqijE6wO\n70UG7USeCj4Gyu0Iy8Uxi/khIQSODs/QOr4hrq+v6F3gn/2Lf8H5V1/x8svPOX97RZUqjJbMZjOa\n3RqBwwboBhtJcEHj7IgdAyJIFuWCsihJlUQlkQEhJJRljugHbm6u6Yaetu1ZLJfIpGTwPc3Nlqcf\nfoN/9ru/y+vXr1ktF0g3YJTj4w8ec319TbBAllNkCVrFN2+732F9nBMezgsY9jTbgXmZ4SzILCXN\n8jjTHz1jgKHvcAHK8oCA5fGjh/zhj37MixcvuP/gUURguhpjNQerFQ8fPsL7wDj0fPzR17m8fM1P\nfvYZB4cnHKwEJjHs9y0BT1WVWDcCCdttS1Uatrua7b5mv6+5vV2TZRl5liFVbAomWY5AcHh0Rtc0\ndP2am03N5fUGNwx34KCXX8Xst9FzPvr4e4xdw9u3V4zdnuK7BUFKZuWCMWkJ+w1CxAWk1ZBlKda2\nJElBUWTTIhBr3M56ejugsRgxcrjIUGGk7WvW+zX94EjSOUWeTpslzX7foLSiaTZIGaORnqjwkTJm\n601iyPOMqiqZL1ZUsyVGK4wKPLx3wuHBKlanZZRvpkYx9APbvgHAj/HkKEQ8wQ5DpLpFFnHNMDbk\nKayOl1RlSVEWSAnWx/m3mGaw1lvsMEYTyTQXH9yI825KR0xSB6HJ8ygEFVKQphEToENUTPnRxveO\nD7zT3AihYsxUxSSEEA4ZNG3bY3R6dxk/jnEB7toBpdxdbFSrP52h8L93EV4sFncLMESV/W/91m8B\n3L0I/+7jn/7Tf8onn3zCo0ePAPgbf+Nv8Hf+zt/5D16E3fQr5m5BTsYCKeMT1wq0Ae+iXZlgSaRE\n5wlaerI0xXviwqwUiITRBXrrkCohLzLqposWh7LkfD7j4uItdhzRytBPc7IgoR8sWZ6jlIgxGOSk\njgloDQEHwpPnKSYp+W//2n9HmqT8xb/wQ9R0FEvTiMAUIkKA6GqUlgQfX8MyX9J3FmtjtRQdNSxB\nAkIxjj6WUxBoU8Y3oA1Us0MyAT/8y3+F3e0N+9sbXr34gu3VOYaeg9UhwfWx/58WdC7O223QtP3A\n0HvGMTCrZhwtlySpppjnKK0YBodJ51A33G42vHnzlg8+/IjZfMHF9YY0X3D/+Jif/exnXLx9w6N7\nJ+zWNxSZ4mBW4MeB6sExTb1nt9tgpMBMG4jUSLrNDmNSqlSxmhdURRYznEbRdh1CxuO9UIqsmCFE\nYBhGnPNorRiD4/GTR9xu4iXW6ek9vnj+GvYDf+7PfR9vY8bzg/c+5OL8Fb/yK7/C//a//M/s9juq\nsmSxWGLSnHHsuVnvYx2+GxiGLbfrT9nsat68PWc+X7BYLEhMxtHRCVorlssD9vs9TdNyenqPi4u3\nbOsGEQRt17LfNdjpJv5f/P7/wzB6pCzo+hgfK4sUwcDjTcPBLMGkKe1+S3ADqZHY0ZHOigiKTwxC\nOJr9GiFis61rRpyLi8qyTKmyhOReQT+MOA+b7ZYXX13iyLi93SB1EvkHSTR5ZHkeF2QVCWzOB7TW\nVLOShw8ecO/sNCY5ioKnT9/n/cePKNOUqixJjUKJEKv0RERA1w5TagC6bmQch7vs/DgOMdmgBctV\niVIVSseSSvCedqij+Sa4SbYwxvyviIjQd6kH76cihYwXf/FLw4OKm7Cxj1ne0Q3xzKAE4+Answ1I\nFduSPsjInQjx8yiljrv+4EiyWLjyHrwIoAIuTPdJ7y407H+mnHAIgd/8zd/kV3/1V4G4S/6bf/Nv\n4pzjhz/8IX/rb/0tjDG8efOGBw8e3P25+/fvc35+/h/+5BKDMjF0rWSEgIQQ5ztaKoQIpDqQFPld\nlCSCuKHM31mNJWlSEhBTq8jT9CO9DYyjpBeCMk3QZUGRRTbt7c01TdMwDhH0Q1BxF+4leWEwSrDf\nj6SJYDHLyLKMk5MjiqLgu9/9Lk+fPmU+X0TWTFCRi+qZTAkaZx3DEDBJgeNPmLNCJQQn0ElCkAaj\ns5hYkNM8XAmy6Y0XbbYa53y070rIC0VhMs6Oz5gXMz7//9h7k1jLsrTe77e63Zzm9nEjIzIjMrMa\nqKwGKOBRr3hgPZVlGWHLMpYsBgywVBIjMCNkM2FQYsKIAQhBjWCAxMADTHkAlgz4WVjPhmcXUFQV\nVVlVWZkZERnNbU+zm9V58K19biSv3qMrySnBlkIRcePeG/ecvfa3vvX//s1XFOP6knpvQcqeMGyx\ndUPKhm0/EnH4bBhGxIRFO2Z7e9y+fQtTZ2JK9INHmxpV1dh2xvv2j6jqmtW647WPfA+bceTtt97m\nG9/4BsvZjMvzJ+w1DnxHHCUsMY4ji9ah80zc5Vo5ch4fHHJ9cQ5xpDGa2ipy9mg0mcx8PqNuW9rl\nPj4rLi+u6buBk1u3uLreUBe3K623hBB4+vQpd1yLRrO33OPW0SFtM2PWzmirinsvvsisNfzgD/0Q\nf/iH/xsJjXY1WUuq74O332EcPU+ePOPiQvBopRTG1VyvtjTtkpNbL7C3f8D19TXfeONN6rrh6uqa\n7bbDuYrjk1PefPNNsrJoW1OCENl0Qh+zTnyvh2Hg6nqFc5k/+t//hP/6v/xPJVUjRypnaJoK7yWp\neLFYsFqt2Gw29H3PYrFgGLaEJKYz5EgYR46Ob+FcjbGOy8trTg5fZOgDf/FXX8c1c8ZxpJnv0Y0b\n2sWSrus5Pj7EaM0QJZfu9PSE9736Pg7293nh9IQPvv8DoBK3b53SVg6ThGUUo4bkJFVjHMnRc3W9\nZj4X1zjtMv12Q4xBTPVNYrGcE+JI8D19N9LMWhJKPhaCGNJXDmM04+gxWpNQiC5CBuXOVSWlQwJY\nRSBSk9H0nadtxSiprueiYiXvvJnlMRPmkw8JnUFbwa1zBp8SGUVV1Wy3fTlxJMiyHidVnkKGjs8J\n8/5R19+rCH/mM59hPp/zEz/xEwD88R//Mbdv32az2fBzP/dz/Nqv/Ro/+7M/+/f6Aa6vr3eeq9Nl\njOHOnTuEMIJKGCMFoa4dZNGSO2cxRiJltJZp5bw4ck2XgOrTscaTixrGWkvSlsWs5ehgXwZTwyAR\nQE3F8cE+l5cXnJ2dEUaPs5ZuvSESmLc1WsN8fsrLL7/CJz7xCU6Ob0mXrE2JqanLUT9JBHqRMPsx\nYm2mqqQI9cOIsgpji3cqmqZZyGReaVAalRVKi6xTl7BSbQwma1ISiWrdWKzOaD8yxJ7GVdy7/35m\ndc1f/cWfcnjQ0A1bHj/sYBvIKrPaBpTR9GNmM0ROjo+5/cItTl44Yb6/wLWWt956m6fPzjm+dUqz\nt8/B6YLVtsM2c146vkPnI2+//Y6kV2hNjgOHixmNTUSvuL48584Ltzg8WGKtY7Vu+drX3yimSKBQ\nHB0eo1JkuZjRtBLx09YV4+gZfGCzXTPEyGyxz+HRPprEO+88JkbYDp7941ss5jMur7esViue/eVf\n8Or7voPv+MD7aJzjcLng9OSErt8y21uymFuODw947bXX+J8/97usrtdY17CYzTk4vM0b3/gmV1c9\nKEfXe5RWnOwdcHh4xMnJCTHAkydn5CTeBSFk6nrGfNGyXl0zDAMvvPACjx4+Zr3p0eXehpBxVcVi\nsSSkxDCOkDJZJ643I1/9+jf43o9+sBSlhLEVy/192qbl8vKK9UZkuu1swfV6Td8N+JQxxnJ6eovZ\nbMnl+QVHxycs5gv6ypFC4Ghvyd3bt3j89AJ0VaS4NW074+5LDW3luHvnBTZD4jtf+xAv3L7N8dEh\ny8Wcedtitaa2VqhZUYq9UlpCVFH0Q0co/tiHJ0fFUQ4WBwtm+43Qx8YOrQXCU0rRti1msDgnn7tc\nLPHjQD/0wvXXmtlC0plNNgzT/zlfkKLYhrZtS9XMS3KJFEZXWYyxhCBS6BQSWRsZRsZczPIhKyMG\n/KbCNi0oQ4iBnCUFexg8SmtcVYMSL5oQI7E4JiZEKBvLKeDRo0dyEn/u2tvb2ylh/7ZL5W+FJ3yL\n65d+6Zf4yle+wm/8xm+8q9BN1x/90R/xm7/5m/zWb/0Wv//7v8/v/u7v8uu//usAfOELX+Dnf/7n\n+dznPvfvfd2v/Mqv8Ku/+qvv+tiLL77IH/7hH/6dXsA/X/98/fP1z9f/n9enPvUpHjx48K6P/fRP\n/zQ/8zM/83f6+r9TJ/zLv/zLfPGLX+Szn/3srgBfX19T1zKUCiHwB3/wB7z22msA/PAP/zC/+Iu/\nyJtvvsn9+/f5nd/5HX7kR37kW37vn/zJn+THfuzH3vWxKX343/6v/xNh6ICikEkRA/hhBKbcNr2T\n92qtC3g/uZqJzi6mSAiymxrrBBdzDeM4SIx2CHRjIGqJPr++XuEHTwyRtmk4Pj7mYO+Q/f19/Bip\n65a6rggl8sTZqujME85VJUVZprcojbWTlFICCkMQ6ptpHEMUBsDH/9UP8qU/+zw5T7hX4ZQqkWVO\nmniRYMr7I6nFufBIJQHa+0SUDxPDyOXlYx48fJ2jw5bT0yP+7f/xb/jrL79OHzJPz655dnHFJ//V\nJ/nQhz/I4eGcqoKh39Bt1gSfePTOE3yCW6d36UfB0hfLPb7616/z8OEjHr79TdraofLIK/fusGgd\nX/zCn7NeX/AD/+L7OTraQwOrdcd28Dx5dsXnP//n/N9fXvET/8XHWbYzPvwd7yfpjph7uvWKymgU\nGVs5yApXt2jXMMZE34sB0bPzMx4/e8Zi/5DDoxfIqibnBh8U3/Wxj/Hi7dvszedyhFbCohlTwLUN\nWSueXlyyGQfOzi7pO89mM7JeDVxeXvHo0SP6vufx43e4vDxn3V8zjp5bt044PDzCOct6vebp06di\n5pMis4Xl4HCPF+++yHbbM2w7vvzlr/DOw8c8fXLJ0fEew1bMkoxzZGswrqJpHI1J6LDmox+8x8t3\nb5FT5ju+8ztxTuCmzXZD3/WcnZ0xm8+oq4oQE2dnZyhlODg44Pj4GKXE4+LRo0fcu3cP7z1XVxtM\nsyQoR7U8IdkZ917+ILYyzOuKNPY4o1HGYiqH1QarFZvViqP9A44PDiBFWVsx0s6ELicikLpI9gO2\nmlKbAx/97h/g8//u/0FpwXhTTJAzlasYxgFtNM44NDde2sbIECymKYEjyN99oCrpHH3fAUoodMbu\ncv6ctfgUsUUtm1OSNJskqSE5J4If0URiHEE5snKkpElIQksuykL5eW+8J6y1aGPptltylqAIUBAj\ne4sZ3/ef/At++7d/+1t2wn/X628twq+//jqf/exneeWVV/jxH/9xlFK89NJLfPrTn+YXfuEXBBgP\ngY9//OM7KGI+n/OZz3yGn/qpnyLnzGuvvcanP/3pb/n9/2Nte13XpDCQUsBZ0ZbXlRNNeYpYq8U5\nP8pxQeeMLXiiHOnlCO9TICm5eWgFSbTlTVWLICNF6soSswEduXVwyPHhCbO2xRqLHwN1VctwT1ck\nr+l8hKyIyTB2ntl8jh88tWvwY0ApRy7cZskKU+QUUAacEy+IzWog5LTjCccoqrFp4Dnd2EzcDUCz\nVuRcpJnAOIoNYgxCPcpZ7XyUnTXMZnsc37pLzgOrbeATn/wh/uKv/przqzU+J1754Pt57WMf46X7\nd4CxeCJH6qSZLRx7x3e5ut7gE1QWrtdbvv6lr/Lw7Qf0q0tqoxk2K+7fu8PRwT5XV2c8fPyID37w\n/ewdHnJ5dQkZtt1AwkCGw0ORLV+cn/Hyd93henNNVUNIga4boa1wWgZTzslDpoyk/LZVi2trLi7O\nOTrYx6fMZrWiagznl8/44Ac+wr27L2JUou+3zOp9ZvMFY0laTilR1Q3zWYutGxbzA9559JRZk7h1\n5OAVxfd9/HsxVlgpw9Cx7q45Ozsn+MDl5aXE4Si4uH3OerMheM+YNqyu13wzvMV6teH68orLy+sd\nTzilzHy5oG0qlDX4JJ4mzhpOjpfkvubyYs1HvvODGJVp2hkpRebtDG0c6/Uj4csry97+IWdn51jb\niGtYVlxcXFEVuMO5c66vN8SY2Nvb452nZ4xU/OD3fIJ67xaYBussOkWU1RgFScFiPmMxX7CYzyVW\nCCU0wqpl6Hv29/bZjp44Ci687jpQWSS9uazV5+Yb1kosfV2L9/PgPeiGpp2hyBACSiVmsxnjODKO\nW8AwDoWWlhIxidOZs7ZANV6ebwUhJWLfsyXTNnNiYTOIulY2hJgTKcXitihww6TAVdqgssI4B0qa\nsTBGMJa6Fhk4SuFDIKGJpQmETIqStALsMg//odffWoQ/8IEP8KUvfelb/tvv/d7v/Qe/7lOf+hSf\n+tSn/uE/GaCxkDQkiap31hFzQtkKqwEC2mSMsyDmSPh00zUq40BrjK6xqJLcrMGK6EBhwFqcdrgI\nM11jlq4Y7mhygDwqKhxxI6IFbEUMZepritzSOK4uLqjrhqHfirVe3Qi3sHLkBDFJZlgIwrE02u6k\n2DvqzRgkYsmY3U6cUkIrYUQorUoIpmKz2TKUSfAqrIWvqg1dL/jbfLkgKUvvN7i65smTC2x1RGzm\n/Of/zX/L5z73v3D14AEf/thrvHBXUpVzrogxsL+/oLdbBp9IykLdEHziut/yzsUF33x0zoNHzzhy\nIwvjqezA0Uwx9iu+9s036ak4vH2Pq3XPZt3zxoOn7C/2iN5zsFyw1whf/KMfepXjo5ZEoNsOkMCa\nlr6L9FkSMuYzRd9fsVjMmVfSSW3Orzk9muPjjOtNz+Vqi6LGYbh1eCKnESNyc6o5m6CZLU9ETKMz\n2mr2lpbRw3bT8dLtO1xfXzP0krJhlCGHJINPAgezGcclzTkEkc0OwwBKGgVhHmiatiWlzGaz5cnj\nx+8K+vwf/4efl5NbLVP3YcgYbSRSJ/b0189442tf5Nn5moOlrCOtNV2M+BCYzRpQma7vSCQxs0mB\n/eUeZ0+e0jQt+/sH5FZSu//883/BnTt3mbUtTW2JQdbM8qgmaUvtaiqjOT44xSkxSBpHcVTrN0VZ\nVzUM/YC1gdlsRjeG4v2QaeoWYyyoLPh4yiJsKh67OQVikIIYgxfNXO2kuQiDzDdyxFrFOHblZDl5\nANtyqjUYE3bNSMqhsGMGjJFZidYw9CPbbTHqKeY+aOlmjVWkIKfLlDwJjcpATsWatiaMkuQy9l5Y\nETGz3XQ7/YM8n2JVO45dUa9mIt8ehsR7WjGXQiBHkQsbpUSxUeQtClXeMAnuNNqgkJsgl8gPtdY0\nOwxbCNkASaVCp1EopzBJY5OA/DHKDQplsKZQwlfOGcaSeAC7mx1iAAVdtxadepYobq0sKalirSfs\nBmsdiSybgOa5uHOoKlt0FcbeAAAgAElEQVSOQOK3KkkBmRzTjiQ+li5E4JbEOHrm8zneaxazGXsH\nS2ZtS1aJ0Q+k7JjZls1mzWrTcbFZ8+K9l/nR/+rHeP3113nppRfpek/MfYFMEk4btG0xRGLSKJ2J\njOQETdPy4Q99GJPBDWdsnr3JonY4I4yNupqxXBwyjgm7dFjrePDwCeqOpbaG1fWKeRnenN46QRsR\nQ8QYub7aCBF/FLn3cj5j8J6qEmPuUGhK88WcmDOXV2csl4dsuxVjP9A2Rxwc7DEMA7NFy2J5wN7y\nED9GjK1wRqE1+NAzDAGFpXKOg/19rNF8/etfB8BHGfZutz1VXWOdLe97VzZIRduKIf049NSVYbm/\nz6ZbkXPi1uk+s5nZMQUA3vf+l+iHgaOjI5p6RkpG/GxzwqiEiiIDfv2rX+R6dcbbD55y6+SEujGs\nVms2qxWuEl+Kp4/fwRjDbN5grEYZeOvBm/jocbXFVY7Ndk1Igd4PHBwekNeRfthSV4bj07tY46hr\nTfYdtdU4qwg+Moxdie2S5OfZ0XxXiISdUO/YOVA8FYwwpVLMOxiwrqvdWk1JNjTvw469NAwDy1ZY\nDsMw0HVy2psEXc9//+n/l9+L/WzOxOR39DJUJuWIVqZYfYl3hFhYFml0DPL5JQJJIb9npVFa0bQV\nKMO263ewq/jICNQi8nRxYtNGvCa+Hdd7ugjHFMgEFJqUBC+avEC1FpNxUb5p2qYpWOm0E+uyk2mc\nqqBgsiknMYtXjlSKrDiVRbb9sCvcIB1LuX9ypEmCMWtni/Q3CV6r5eczVj4efAAV5Uiro2wixoIW\npZDSirox+DTtKgI1yDFJbPcgl907YpS4n4Uo7k4xBUY/gMrMF0JyX1QtzmlCSvTDlpDCTgI6jAMv\nv/wy6+2ap5dXvPHG2yiteeneqyJf7jyjL4nBzhGzwY+RwUtGnbKO1hlO7h+Tc2JzfcnV44dcPHyG\n1ZaD/X3CGBhT5ujwmCGCyhqVDHU14+7dl1CFGtRtVpwci2GRrSpU2ay8D2y6rXCfvcdmw9VqjQ+R\nWRuJMdE0DYvlkoODA3o/YuuGbgg0bY0LFbauuLw45+DoiOXigMq1aO122WPkLFJXn9DKINH10uUc\nHR3QNB/i8ePHXF6KKCMEceOLKdE09S7bLASP1oq6rpjPhR6Jyew5CWc9PDzi2bOnck/LKef27SN5\n8JOwWg7398g4iVGvLTqPfP9iwfd9/w/Qrc75+lf/mocP38aHkcuLDV/8q78UqfL9eyxmdck8U2y6\nDc/OnvHGmw+4dfsUoGTCWU5fOGUYR+r5HFs5Fos5R0cHtE3FerVG48hh5Or8CltoesZYmkZCNIUm\n5krhEbpmCOJU1jQNVVXfnNa0ZrvdMI35u66nrptSsEeGYRT71sETgpd1nSlFWbi6SmX6fixfE0rh\nFZGWQJCSZSdwnTCfrHFQa5mFxIiK4BWEBKY4rjlXC8acrJyGtZx0tDaAAW3wIZRny0uf95waboIC\n67qmqqrSzQf6slH8Y6/3dBEmR4xW5BwYh4CeulwlmW5KaZp6tqOFaaWJUYpXLm+iykAwggPHWCKT\nUuGqQIgyWEhRjodKgRi0hx0PUBU4IAPa1oJRagjJY5TFJw86YawUYm0k5qgfBpS5GaSl4sakSm5V\nRoO2pehCCEORSqbi3JSAJPSZMIljIkpB21Y7us84jmiVyUgEjVIalTTD4BnHkWHsMVZI/7dfuMtm\n0zOOA+v1Ch8C4+jpukEURtZgjcHZCh8T+4dHzOcLtl1HCp7KWKrFDJ0DOYrRvdVCCwoh46Onti2z\ndkmKoHAcH58CmbDd0MxaPvqRjwDQNDPGMDBsO7rBC/aWErO2pW1n2GJmv1qL1eLduy+htGP0CaUN\nBwcL8tWKw31NPxpiUFijOTo44vDgVAaXWe9csKZuOhV+bYyetnRjKFGm3b9/j6atGceRuqrJiLfG\nbNbuItsnmmNdi6VmjBHtJIEl50zbNsznbSlSsoju3r1LDpHa1IBlDIqQ5WQWU4RciyyaiL51B2cX\nPHm2IsYBrWqOj2+zXp3TVBXBezFFr2bElFjuH3D7juf49JQAbLqO/cNDEmCqGu0cc9fQ1A191+G9\nGEJdX67RKnGwv4cq9M2cVCmqpgyg2wIRCJ6+Xm3wXmYPqUh7Qyyii1xSaJDI+qlASwK1eS7yvjwT\nKQj1LVMcD1XJfpNnTbrg4mwYVREseXTxZxG6poKYaFrx/jZaPMhDGMUHwpoiGkkorYsHsCrPmSaW\n/zMkMWRSMWGU+H9bayHn3Wsa+p6UM01dU9U1PFeo/zHXe7oIG9FJEEImBo+tm/K6FVrJjuZcRe3E\nOSmkBGjJlcty83NSjEFSMKSLEWs78g2TIoZSjDU7TGk6Ak0P3LRwfMrEBKPvQSWqpsIZQ91aIJOV\nMB8qW9N1Q9l5NT763XEILTlzXT8Sxn6HCW831zs82FpJwEg5S8KGNiitpKPPkuZgjAUS7axmHHrZ\nJkqHFjqZMnf9ltm8LYNMse2zrmK+WHJweMxqtWIYBoZhIATRw2ulmM1kIHRxdc04CD5LCtjK0F9f\nYXOidjVdr+mHkdrNiSkRs+byekNTX9JXmoODJcvFAd/85jcgDnzg1Ze4fVcGGSIzTwyjGKfsHxyw\nt7fH/t4eQz+I2q2u6bYbLs7OiEmzt39M13fklBg3m+JkJp4YSWfu3r3Ncr5Aa8cU/Nj3fbEizeWc\npEhFHZZyEjzd2PKgKl599RX6rhe/khgk1bgUExFwIMfYnAnBlxNETeXEUc0PYk6e0420dTHbJ4VI\noxQpaWYzR+cTvY+oZARysmIYHvstR7dewlRLxk3i8ETer81igXM1q6s1870lo2lQFk6Xh+iqBVPR\nh4RyNS+9/CoYRzOfo0zFyfFtDvYPaeua9WYQz4XacevkiBRG6rpFBBEBpQ11VQs/14jpz+gDQz/i\nqgZXFVtW58gpE8dArjSublBaSooxlqqS923y+R3HkbpudvBS087KEX+k23YoBVUtzZQxBSo0BlIi\nZfESD1HvGAria5HwfiiJMTKUF2FGlo0BGehqrXG2FuFHFoOiEJLACrp8HVIbtJNir8q8Zhh6rBUX\nPIMiJjEOqur221Ln3tNFWEzaFdZWtK0pBcIWx3xxtk9RjiJkhQ+UNNVSYLOY/oResOUpoj5PGANy\nLFVKfH6tc7siqIuhvNZqZxqSUmKU9g5rFfO9fRFSqIS1GqUhly42FXFJLA+5NRpTEmFVwXb3lnN5\nDaXlrp3ZFYMcA0prjFKYSjpprRVaW4ypdx1F3QiboqpkWDCGxHa7IiYhl1tndt2biEMMy/kc7z3O\nGk5vHTMUGEaMUXzp1sVnYL5YYLQuhtgKlT2n+zPeeevrbFfnhLFhCAODD9h6jl91XF5t6Lc9H3jl\nHj4oYlacX14Thw3f9z0f3RmjxJTox0DKmsVyH4jMlwua2Zzl3gHbzYZ+27HZDljXEhOcX1zhnKMb\nOuZLmap7LxFTMQysrs7p+g7XyAPWdRseP37E+973Cv0g8VHOVeKbGxQ2ykar0FRVU0z7U3H/UlSu\nBq12x9Npc5bwyLjbyEMv/25UvbNZBJHUA+RgsMqQkxe61zgWdZbF+0hE0Q8jmURrKxZ7NT/0w/+a\n//NP/pj53AmbZ1azXV8y2zsma8W2z9w6fYEvfulLVFVF12Vm8wVLt+Tk5ISDgwOygjsvvsSdF+5T\n1QuMazk4VGglQ+FMoHItWUmKhDUyl9j2I85VIhIyGmdq4iT1TVmUbEmGX65uyKV7vAmbkDmKnjLc\ntBUqWwlRHceJzZPQxrHcr3fvb86ZcZThd/KephHYI4SEtk68TxBjIKUts0VNSuIRLMdXcTMkJ4EP\nbU3OkZBEsap1cZnXwnwIKcp8iUTIidbIC8k54pyjrt1OMj3d95SkQ/92XO/tIpwUYUw7wxtjxHgj\nR10GAYqEcH20FgwyhWLsoaQrHsfI0I3kmMrxRhab0TLocq6SoqAgIA/VlA6cc2YIMgV3zpFVRutM\n1dRirWdh8CNNU6GtLsVdgboZXKDFkUrpYsapFENZYCBHpKaWolQ3rnxc73ZhrQ3b4jSVswHE9Ump\nCTeWhZBJxUc1gUrF00ICTKejXV1VhKywKhEJBD/SbaSTyyhICZ0jWSGQi5JjeogerSLWSCEJKXN4\ndEh+Q4G1jNst3egJYcvF1QaPYuxH+pBZbQbmhyd810e/m6ODGVWjWG1WAGKsbUecy4Q4sFjucev0\nlL7vGUYP2tCNnqv1lqZqePz4jJAUdVvhfc9sMSdEGYQGP7C3d8Szp4+4/3KPObZYq7CLOS++9AIX\nl2dF3eVxtt51ZJNh/8Q1z/nmlDl1u1qZnUVpTklojSX7cCoqRJkvFCLhzut2Kt45CnyFlk065jI+\nyoCKSF1IYmiuFY/Ozjg4OeL++97P5//fP6Xbrmibiq03hG3H7bsv8rEPfYCuH/nu7z7g5OSED732\nYZq6oZ3NaJuZhFyOA9oJQwgMKYs1aE6gVd7xc31KGF2O+URyirtnJcaENYa6akgpQ5aiZczkMpbE\nlSzlkoYDKWZ8lqJrtMXoYkRbiqwpQaKS4mx27/f0787J+i4e+7si2Pdj4c+X02oZrmMVKiaySigy\niizagpiomoY4esbBIzCwQiWEVaUVo/cMo+QFqh3mnHdDQ7GoNbsCDGCthJd+O673dhFGS7R8KZ4k\nIztull1WTXBC6XJjkNgRwf9iGdyJn0S24iPx/OBNHkJZPCGG3U43CS9ADHSck+BHrRUxi2F1zoGQ\nBD9WBdONmZI/BxSbPVUKeowJXUIQZZE5qjJ1nR76vcWsbDhph1OCQrvZ7uvePTBIAosglCBV+NGA\nGN8k6fRjFgMVYxzJj0CisSJc8VYX9kcWvExVKANKZ7bbgapqUCqjUVxdnaOSZ38x5+jWMc18TttY\nDpb3qbTmr7/2Jt/7Lz/JwycXOKP50f/sU3zPRz4KpiL4LV/50uf54hf+DFeJlLeqamxV4a837B/u\n4xpL70eSUrTzGevrDf3gqaqG1VpEO6PPJCXJKbP5jLqpeOvtR6xWa26d3CGMPZeXZ+wdbalqgzGJ\n1eqS0QuzYTaTAjybLRiHhFbid6vQwrpR3DzYOZJyhCwFRNZdYcWkSAwRSsS6NsLnjmVTSIVNYyfD\nFxVECJAVWWXZLKWnICcvFpPjiFGGzXaLcTCEgfuvvsIr77/Pm2+8wYMHb7N/fJt791/kwx/+KNYt\nJLSgapgvlpydPdv9vaj1aWdzAp4YMrVtMLrGaEssXHOtNFkFjE5iP1/8FFrXSugsmpikccxKg87k\nJJ69lE4wZTGnTznt1vtUtCYR1c4uoJwqp5DeyUdjKsAxhNIIiV9LyrEEbApHWmmxX9W6WE1qK7Oi\npEg6o8r9QiWcqojKE0NC25rGtrsi6spQUVsjxbtAecLh97ufRwRRksxz0/gkghfDsG/H9Z4uwqkY\nd5jpyFeiqGWwJi5mO9/RVAqdtjutty24qSvevBNGmFIWd//gGZ+DKMThbmIqSEvkrPhUpBSkA9UZ\npSMxBIxyQupGdOQxTl4ViaZQcaZJqyluUKEsMmutdPXPO9Gp6SvKX2VKSK0rYoFQnucuPv+1WlmM\nyWQlVD20Esc2Lf+P9+K4FUvRjjFADIRi9i2cSYPVwhogZ2oLMQ6QkkAXp8c4q9muV7zvA+/je37g\n+/G+J/YbDpcLOg/RzOiiQefMoqmYVZbL6w1kxRtvfJPT26cFMhJMuO8G6SirGm3B1RWX55c8fPAO\nR/tHhCCbgS+uVZdX17RtzcmtPdbrLSlFzs/P6fvA2bOnHB6e8uzZM45O15g+MYxrtt0VJyfH1E3p\negtuOWsXgC1c7WLjGGK5EaoMT/POV0AhcJjSoLV4miilSyp4T2YgplCGu4U9k0pnmHuSgiEIbBVz\nIOWpwwvkFMnRk/CgMqPvuH10ysZIK/jxT3ySTzYNOSdWqxXz+R5pzAyDx5iWi/M1e3u3CpRib3xT\n1DSEzuImpgOY4vGLPANicm6Ik9FNhKxiYQ/cJCiP4yg84bIZ+UlMlMVWNmXJ0JN7OzVDaQediY/E\nWNZixFY3s5bp2q3ngtPePAt6R2GTPwvyEEPCpywmQWTSxDTKaneSjP6mWCrYDa9zvvFWrptmZxRv\n9c3PM6nmJvGU98VWM2ca+09gMOeTYghygymWfSoXxgOZmIZCOUvFpQyUCoDCao3Tsthi0qDlIQoh\nyJDPaFKgBDAqjFZYY0FLN5SVJuZA3VjqWS0DGKvQVAJ9GJkOp5TR5B2vN6WIVhof4254KKkAN+mw\nTll00oLLPUer08qVnDcZju2aXgVWm2LqI2h4+YMcK7WGYgMozBFKdyavG0CbTEgjs3aGymVz0IpZ\nO9vFd0/holPKtTGGlNnxKGOIWK042jvEGM2mH5m3R6hqD2U1tVEkbWlMjQLCOND7yKJtefDm26zX\nF5ye3OHq/BkAl5dXbLsNrqR/OFvRrQeGPmJMLT6lWqO05uj4CKUyxhqcramqPbqtqJ36PlE1Cwaf\nSarman3Nw6dvUtc1y+WMO3fuU9e1sD5chdFGBjTKTSQZYaoU60M5Fch7qJCCOw1PxZApojQ3+D0y\nNLK6RilTGBhChUzF5CXlgilPBSBldLlPTiuquoKqLbzUWpgNRlM3QudTWjP0A8Za5vMjyOIyaB1Y\nY6maGd22k8JjDTpbgvfELCdECs1eWUU3bHYsHpTEyGutiGPEWSc8WVcRfSBFaJuGbdeJOjWNBaKR\nNRG8MBxcFqx1er3GNqBEsuyDx1rD6COjzwQPxlb0xSPYOVHBCl43Ud5kI1EoSIGcJBVGodDZkBKo\nlDCmom4sMfRElVBZiUouZ2KxQc3FqoAwYqxIpH2Q2gEKP46SuTeMOCtp6DlKzFX0IyoEVA7Eocd3\nkj/n/cjq+p9A0GfX9XTbjhwzOWacrdBKE4YRPwapxqWbrFxFXYvMWJvnub5JfImjHNlT8EJBSxFb\nmAumpHKUWV7ZTSPGKlRVzIvJJJ1QWVR8WgkGrMtRPsUoOyy66ElM4VYWy7vStepsRL2XhUub082u\nm+KE3T1/TVi22h2JyaC0TOlTOT6LvzFCWk+pdMPSuQNoAyaDRrPbvwuuOT2EKQumpyvpAHMWaYtS\nBWYxeqc6VEoxb2oUBlU6AqUzdiL2F26p0Zo4bDl7+oT9RYsfhT8K8PjJGdrCbOHQZPrNwOXVNWNI\nDN6zevsdVNbMFuKd3DaVdG22wtoFXTcwMzN8MMxcy2LvWGJrtAaT2D/a4+ToBK1FFWmNwxZsVytN\nSL50rUUVpcoOJm8NMd3g6zCdRKS7lM5ZujFpnKwMdw3Ywu9+3k/AuRmqKCUBXKx20NKEPYuox5CV\nw6ALG4Py/yoobAOttHSuIZOV8NxjimgjjIVQYAJbW0y+SYGIMdEPwlSIY1+O9BZrHdvNSCq4NhkG\nNZTXU8kzpTRVI6q9UKiEWusdA0L6orzzgpANTWAa6ypiTHTdQFVVNO0cV1k2wwpVXn+MYLUkVwTv\nRTlaCr2zNSjN4GUTSikXUQbkEAlAiiKZVVrmKQmRNSelhLUTM42VTTbGVLwiYnFoo7glCuVws1rh\n+45h6NA5klSm26wYhy19tyWMPa9/46sMKfHf/dR///eoaN/6ek8X4dhtCJs1wUvkSFvPyGT86GUI\nUDWoWrqH6BzJOaJxJCWdYk4Qs0JLFEcZYiUqI1Z2WoMz0qVqLXgUypCJgMHWxbs3BJS25ImmPHWX\nCbJSWKsJhXyujSVM+K8WiWdKeZeOra3FJMFuszSzN3AEN3zWXeHVRSCCKlP6yaCoMECiLDzpdDLT\nUbqMQRDgUboqjUFn2QCehz6EW6kweapDhRo0oSRK7TTzu58TcLbaDVJyLiSf5yCSCToKIbPtB5rZ\nkpyCJBYjRuD37t7BtYpxO7Dddjx69A6ubsloVqsV9+69iMotq+srQogc3zrGqBnGOfSYePz4MVXd\ncLB/xOkLd0jRYuu2xBod09QNOSustmJOsxuwKdJzstMJ5rkZ0OUdQyYTdh+bTgvvlrSC1m5376yR\njlOUjbJonK3L57Eb9Dz//073fhIYoey71oV87iTskZnIJG2n3G1I0i0Xho8fQ2HG1KXZSMVwStP3\nHZmMtbIhbrqxwBiZ2aykv6DQdUUfAhhDTOL367Rhu90SU9qxh1JJutDTUV77HYZqjCX7SNUqnFPk\nPOLDSOOEiRGDSJwTMr8RH/WEVpmQxZYgJRFGGaVRThJhfNlcrK5BCSyYYiCWQX4IHusslZHpTkqJ\nq60oQ5umZgyRYehZtHNG36Nz5PzJJaFb45zCqUTXbeg2Gy4uznjw8C2uL8/JOfPonbfYP1r8B2vX\n3+d6TxdhPWxxYcCQcTqyunhMP4zMZjPBcLKHXO+GT4P3ONSuC1ZKCfCukhy7YsZoi6sldwqkiAap\n1gXnKDhfkTUnJeEBuqQdTxNwpZQU5Sy84Zy1FPwyNCQbyHo3KMtZFQMgcWySRyaRCJSRCFkLH5jd\nMFIKflJxx11WZYNRqgz+1ES3U6jnfHp3XXMsxTkLpc89L7VU088mX3XDCimd9HTlCRp59+eLK1zZ\n8MpQctr8lDJCxQqebusxpmG12lA7xdW4AWA22+PFF++z7q5I/pq9PSeSVKQgHR0dMZ8tsCazmLcM\nXcdysU/V7JOCZasin//zL/DKqy9hS7HdbgcO5gc0TYtWFnmnJ5n7zS90xir7ruI6bSiU+yu/a4yp\nSrcWnyvO5Z7qkn9WDMOnc38GrK0LdATWCD0xZf+uYeyUk7Z7q3MmTvex4KhT1zlRFkWyO+FvxXUw\nKVROaMFRGIMvzAOLM1XhgBvqVtR2TSu8c1HGjcyW+8QYqaqKcRButLOO0UvasPcBfMBvNjgrQZvT\neyonKVlX09B1iGJuI9Qy2cTadgYKxmGQ7zHkwnsXxZxRmhyFilk5R9f1jCoRh0hMkcpWjCGQs6dy\nFluJC9um36KUzH9ErBUxRmF1RsVBsikn5VUKEBPDZsR3G2qjicM12vcMm55hu2Fz/YTN5prz83Mu\nr67oho5nz54xevGX7vseawJa/RNQzO07TWcyPiaSzSQHrXU08xrnLEOGPmxkxzcWO5uRU0UqNDFd\n+C3ZFg4koJXCEElZsFeZd8kN0tPZG3lsQ5RYJDV1R0qVsMHw7sGYMtiieZ/URiFEjLnpbjJy3Pfl\nQTbGgErkHMl56shksU2y7MmkJRvBp1O66XKVkmKe002xUNrumCQqK4w2hTY5fX+hueWpoJZGa4qM\nMdrJ0SyE3fDl5uvy7s83x+iEUnFXlHZdcUrSSSslqjktqrk/+7/OOT7a4+033wLg7t171M0c11QY\nZXj86DHHxyecXay4urzi1VdfpWkarFG0TUW32dC2C+pmwdArHj58nafPzrn/6n26bc/TZ+fsLU65\n++I9FvN9Me021Y7jnVQqniHyc+rnut6/Gdp483qFeTNhl/G5YdRUHIHdQGwaKD3/b7K2RAkpHe7N\nexZDIKdpQxU4RGWZZcjnyOA4pcmq9bmY99INKzWZP3nJU0uJcRwKx7VBVny55UnfdOgJxmEUZpBW\nNO28qP9q1tst1gaGYWBvbw8/9Cil2A4DrlDWUlmrTe3EzCqEIpiC9UbYLHVd74rsZO/a9yOjTuRs\ny8YiryFrOaWlGIl+lIJqLDFl/Ci+EN4nVIyEMZBjIsVA1grtKmqViHEgJ+mYiSNKJXQM+KFn6Ec5\neabIZn1NCiNjt8FqxZvf/DpxHHn06AFKDXT9mk3X4ws1pDKSEh6zwGEpZhazd2dr/kOv93QRzmFA\nh5HWGEmQ2JsL5qMFDzLJY1Ii2YTWARMivovlyCia9JQhWEfdNHRBdOF7y/0dhgXCiQ1B8EEURcJY\nYSuLUo7KGsgJP3qJRNGC+1IeNK0y2Y+kLBP1EAKVdbJQCx909B5tDK5y+BQF87LCuZymwD76m4ly\nTjuppykKuxhzsd5MVFV705XtMOIkmK8CknBTxYFNiod1VuCWJAOJ6cHXVknXET3GyvQ7hIx1wjSZ\nIqWcq2SYUSh2E81pgk+cs3RdJx1xCKUbrDCtpWkkMPLxOw/5xjfekPubNet1j6kSlZPo+H4YaJqG\ny8tvsr+/z97eHlrJ+9TOZmgt4ZuPHp7zhb/6IqNPrFZbXrzXkCJ87Lu+hxfuvYxzTSlACmPATHSs\nwl7JOeN92pmz7DbLsiYmyCGlVCTvsj9PsMLzE3/pvOxzUNHUQd9ACiJfzmB1oajJyUhn6RbRpuDu\nEj4pIh/pgtNU1JF/78eBzWZDXbVQDI6GYpQjhxSFda7gw4UdocVyNpX7UlcVRmtW6zUhySnSNg3P\nzi+Yz+f044COAXLm2cW5FNumKUM0gRFGL0q080cXu01sCq3N0RJ84OrigqqqaJuGsXgFL2eHrLcd\n0UraeFWGoTGMxCDeKEbJoFyFTG0rbGPRWWTM625DbQ0hDKILIKJrTegyY78lJ09tFct5jYqRsd+w\nWl1xdf4Mv93w9a+9zltvvEFOI/O6ghToNysJLG0dyUA2ikVlydqitEVXDh/BJ2jqCnLFsv27ewb/\nx673dBFWMUAcyEkTo5bjStIo5UFpGgVNVQ7fSmTOunXkPD0csngHBbW25FoUMrG7EjMRH4ghkOpa\ndvWURJBhJFRRqQqI4qplHc4YYvQM0e9I6DnJ4ja6yJG1JoeySxtLKpCC0RlUpOsGjBU4pB86id+e\n/IRz2A0VdVH9KXNz5DMTbaqwMqQpFiA3EqAMN8iZ6EWaXRtXOuNMHD0qTw+qmBYJeynt4I5cOj3x\nkpXsL6002RgCeVecfD+grGEoD6Jzjr4fZDCZJwqaEPq33Yaz83POLi9568FD9g4OALi4uqKPHUcn\nc5xGDF/UhvOzCzFLz1BVDUZnNusrNustg+9Ybzxf+/oDrq+3+DGxXnc09ZzT05c4PX2Bup5hTVXu\nj/C8VS7+0uUUIM+rbecAACAASURBVAyIifFww2f9mx0xqDL1TzfvO+z+rJTGOVOobfJeTqKPG2Wm\nHLEjoqCbWDEpRTJlyFaO7VEbwTOjFGFdMOiYUhkwJUkIZjK30UW9lcpANBdKpUAIkm8oOYTTphFC\n2HXDMcpwqp61nD87YzGbc311jTbTZiKexyl6tptriS9Kge1qRfCBuq1ZzBqqumIcRpmAA76TuCLJ\nfPNsrtcs5guIiYuzC0KGXMthLChhOIXksU5Tu5Zhu2Xbr9ExE7XD+4EcPU5lsu9ZXW3RKpGjl/fP\nB1LybFcrNutL/LCh31xzcfaE1fUlMYxsLs5wOQKZNnoUCT1kKmOYtZmYApoRryuJRdKgjMCEWoFx\nGiHCQcbQtHO+Hdd7ughrIztfMeQtJsw3fFltSqenhJWgUgmJTBmS+EDYqmJRWUbfkcg0zqE1+Dig\nciIZcEVBprRgVJVxQosZI9lrvJ4WvCFm6VaruqYqCbVV3RLKsDBXQVRVUbrpTCaSGYPH1k4eaBIh\nZ0Ls0U4eQgBjC1vD6N20PpehntJSDCYIQaCAiS8stThlzxhGwZRjxmJkOhwDRpVCQtwNn0KBRnIS\nv4sQo5j4aLOb22klAyGrNaEfGNVQSPeywcWy2YUQxdnM2p3M94ZLqqjbhhdeuM2jB9/g/v37ACyW\nC+qZw1QO33WymWrL06fPeOn+y+zt7WONpe86hk6m95eXa56dbXjrrQeSBO0actbi4VzVNO1c5MfW\nSUfvA9ZN62Yabt50u39zkAj//qB0wsCn932SKk9JKVpLwZr45tPXp/Tugi4DQXYNQnyu+E/cdT8O\nKC0KTvFCKAV5+poildbasFpvyDmzmM/RxpJToh8G1ps14+iLGEngjHEYyxq6UYSOk0pMKVbX12y2\nG/w4AImUFH4Qn9/ronBsm4bVxZZ+GIghin92J5zfuqyfupKTwurqjH7ouTh/zEsvvoT3kWdPHsqg\nrJZ0lvPNJZnEdhjw44B1innbMqwGNus149DjtwO1tRidSX7g7OkjVhdnjP2a2hk2q0tSCqw359Lo\nkFEpiDw8eZRKOA2WzHxfYyIi/pg5tEK8ickitikzHGM1cRp8G2TN6ISygsvHnAn5xk7gH3u9p4sw\nWgpULpaSVhf/TxRmwizLs0WZxKaYCzhfjpfRE7fXWGtEoebjzjnNWEdlHCZ7Rj9QOYsySmJQ/Eg2\nBlc10mmmxDgMKBfFFyBC8BIemEJgGALD6Gmaubj6j6PQaXImIYbw1awpmDAs9/eZ7beYyhYzbKRY\nKCHH3wx/MtZNuvqJpF7SlwvdSbq6XChCgjEqZEEFnxj7QFOJ2Ujf3yRM+CIZrZzDd5JYjFJkrRn7\nofCbpXh3iHH3bDbDF5mnaVtUccvy3gs2pzWqbCKxHNWr2oo7WfUv2W7OOdyTTni5XFDPLM3MsvUj\npplRVVustZycnBSZaMI5i6saLq+uODu/4JtvPeH6eoWxjvlixsHBIQeHRxwcHBcoJRYPhFQSRwJG\nq92vCarNhHcV3snD4/k/C4ww4e7vLtLT50khezerRTbHm0SUGH0Z8mqJ0Sk4/DTwkxNOput7YurF\nFrUc+6dfk590Xdf4GEhkhnGQGCjYJXDnnPEx0K3EuKhtWyKR7Xq7+7mn79X3gvXWdc3B/h5Pnz7h\n8PCAq6tLmrbm/PzJzrTo4nyNHwaqqsYYw/XVOTknuk6CBEBxsC/3NsUVd1845p133uHf/em/YTYT\nGfX+/j4x1Dx9/A1OTk4YfA99T/Y9OM04GB49eovtdk2Kkizy5PwZjx49wOqE79YYFVDZo3Ok0oq2\ndSyWoh4VlkVA6SgG/iQqK01F5ZoS0Nrs1my7aMSRLsYi6NJEZUhZNsCJxSSkD1kLVmesNlT1PwHZ\n8oRvKSMzZmN1AdazeOwqJRQWJXicHNF1oYMVbX4q3NWcSMMgBdw5Wlvw2igKJ5MTeRywdUX2o+Ce\nWmNSAJUggaNMqq2Ya/vtGpQlJhk8tNZQGcrgLuGHjRiC7C2ZVbXwFnOinrXM9hrqWSPfq1whhJJW\nK8wGXahn01BGGA7C1FCGXbeplCJMPsV1TdKRYetZX19TKYcfPNSeMHg630l2WYg7W74hS4c1GReJ\nIjHRTV1fltQE7wPd6oqcM8vlEtXUDNutSIjrhoQc+cdhYIhRTilWBBJOWQ739zhc7tNtpBi0lWW5\nXKLrxLipcFVFSE+ZzRf03Yg1Nf0wYrR4dxhjyShW1yvgRlKqtMWaWuiBPoIOO6lt7Rwxyukko2WY\nWd6zruuKeU/hVmuZJeQkQ1hy8YPIFNXmu7P9nt8os8rEIpk12qCLyf00dxijJ2ehMqYECU3wxcQm\niQG/ypm+HwgplzRgUxz8hMFglGTvGS3rPiUpLN12YBzlFDKOnhSiOIu5Cj90rK6esV5dAwprFaYU\nYGuEx73dbrh8MjCfz1gu5zx688v0/Za2FUOjTdcJTmu0RIJ1ay6uV3JPrKFSsvmsrldsLh8AP8qX\nv/gneO852D/g8vycy/PM4dExb77xlyz3FlitePxm5ur6DN93+OAJfmTWVmw2Vwx9R+UqVt0aYzKV\nSjTNjGahMIws24qLJxdyX6KmshVNo0ssfRLsO8saNEbogtoYfBB4JcRENZO05glPV0qJ0lYVd7gY\n5X7lWKiFEaI0YUlpUuq/LXXuvV2EsyIkefE5JuJ0FtfgSahU5KbFxEdSNZIwDLKYhMiZ2ogAo+Cz\nMStxySpIxzAWlZpzDEHoWcpIJ5meu0HGGFRKMEgHVaGYTOel+8nkcYPNCZchVDA6iGpNzJbFcsle\nPcO7ijyv8AYsaee05WwNURWRw+RnINlz5AJPyCuSTkuJpFsphY6p4MBgK4VmJIURv9rAsCV217TG\n4VIom5UMqtRUKJQCn3dHauvK6/dCm/Pra6rCAY0h048b7HbNuh+om5qLvmd/fx9nHX4Ydw5TrqlZ\n64SKgdRtudfe4vHlQwD6p1f49YbZwZJZvaT3nlun9zi9veL1r71FXR9y+9YBMfScHi85WO5zchK5\ndeucJ/kZ1lRU1nB8cMrJwV3unNwjdJ7KOBQJY3Txki0DraGjaWpU4Z2qOJJTkOGQc/R9x2w2ZzL/\nN8aQgoRFpphxrmX0QQZ+WjEWn1rrDH3IZG3EUyIFcpIB77brOAECYsTUrTc07Zyn51fsHR7z/7H3\n5rG2Zmd5529N37CHM9+xBtdcLjsYDBgwBELcKAHbcUKHBjpNhCIRJc3QjqJOYyFbRiFSZISiNNBA\nIjpttxqp7e7QbZebIQ4FBtMmxCZ2BtsYD+Wh6t6qO55z9t7fsKb+413fPrfcwRTBIpZgWy7LZ9c5\nZ5+9v2+td73v8/yecRR33jisIclnNpvtMXrFMHj8cCrTfq3wTtH3a/ym4plr16gWB/TdwHw+59bN\nG6icmM8qIbTlwGLR4rtTQneM36xQOZKIdKe36bpTchjRqiA5lWZlMrcbxWZzSo6Zm12i70ZmbU3X\nnwCBsRswpsHaGmM0tlYcn9wgq4gxDoFM/T1On/5NZgvL9VsaTIMyntydo0qJ9c0OVyc2x4qDQ0fH\nKedmh4RB49zI0a5nPtujX1mq/XMsZ5F9UxHDDmud6ONVZmNkfOYGObcEG0gl7p7ivotBSGsahcqK\npoJIkplIzrhJGZQTpnJoROpXAeOQipmqXLfbE84dp6QUseG5Ou7/1McX9yKchCpljEG7M2dOlqmH\nLIwYcoqlz1amrBNicAuBL7KuLf5OdjyyJt8huA936DGno+J0tBRqW5RGPVOfMBedaBRXXzqTJAkQ\nRNP7Hm0qduYL5ouWMSGMVutQeeLbTi9zShgoAJlSEUs/s0zey8+OMW2HdspoCTiNiP8fja0ce3t7\nDFqxGUdyN5T3bXL2KeEVhDONrFIKlQtcJkRSEPgJRpgSycuxWGmLH4S7oZQmdhGbM8fXnt2K9aVK\nT4ydZkgev+nQMXLp8Dz3X7wMwAN33837//2/ZfWpJ7lw+RK7BwfMXMMLLt/LU5++ypXPPs2FvT0a\nW3H9mWe5++67GIPm2uF1Lp2/wKWLdzGf7/ClL/lyFotd8CPrrqdC0KGnncik5vM5m75nvV5vj+dV\n5ej7FZv1BqUEJtS2DbduylBw9AIJstZCZVmvOjKaMUSauqWdz7h5fJ35rKVpKnwK8rmphCIRjWO9\nXpOUXBM3rj/F7Vu3USlwPWbmy318f8zx8W1Wq1Nqqzg62KHzp1x79har9bqkmVesT09IYSjW7syT\nH3uK3d1dnnzqQ4Qgm6SzhhBGnjy5VeRkiRg8w/oEjcBmjAGnM2nssBb80KGJ4mhTlpRHtBUQlDOO\nm9fXLJoFN05WzOYWrRN1BW1dsVw27OzuYh0kvY+2km2Xo1zR3/D1XwmqR+tdFDVRdezuniflGc9e\n/xTLnRrLOTbDFcbhhMrs4ntYra6VVlZG55qkE8YEnnzyUzT1PdxKkdvrj1OtR06OR6pFzaA9ox85\nf3CEbSrhoFTirhV1j5hZrNYFczDJPzMGNXWbtvMV68RyrrMuA1SZLZwxpWXAa/WfAIAPmbNUitIf\n1FqOU7qwIMhCUZueE6VB8YxPu93WVSR+/TLpKr/iTC+rtaS0kinuOVV0mWk70BAbdNouWmcysTOt\nrAI2Q0/UhmZnyWx/uZVLuazQ2aKCJuRYXEBF0G+t/E1KXt/EiLhz2MVk9EiSHpKzZNAppdHOyrEu\nerSC2lma2Yy8GBhCxPsIKmNgu1lNbYg73V8wxUBN/cy47Y1OG1LTNPgkBLkQBmbzGUMft9pROdp5\n4pjRKjOrLcobXFGxAJw/OuQV3/D1DMlz/eYtRh958lOfYRwDO1VDWG1Y37zFXZeOUMlgxkTaDDxy\n9wtYzJecO3+B5c4udBtO1htCcwxKE29bLGAm8MwNRTubUYeAy5E4dPRKoErVMDKbtYSugxAwPpCG\ngE5C1lolD3XNerNBF4SiU5Eb6+t0w5rVbTEPHBy2KALjMIgbLYNxNUNS8KKXsLr5aXy/4frVq9i6\n5pkrkaeuPMODDz1ETolnrj/LJz7cc/P6sxgn84Hz54/oNmuuXHmapjJ0mzXLpSQVf+ZTA7O6wjnH\nZuhYLhfEYeBw1lDvGkIIdP0Gty+bjiSmaKzV3Lx5nYP93YL/XBKDx5glKE9mkGFjtqTo0FnTD2t2\ndmrInpASfszEJDjYkALaaLIB6xxjLwYG7wVZWlnH7ePr1DO4cuWYsbdEVnzykydkf8TJ5jMs5obk\nG64/c5sQVxwcarpNz3/4d09z7wvv50tedAltan77fR/gQ1eu8qq//BWcO6jZtZEPfPiT7L1gF+97\nPvmpT3H//ffhrNn2sQ1SzCk1MaLttpqd+vcxhu1aIfeDK4XQ2b833Stpq+nOzBafq6T5T3s8r0X4\n+77v+3jqqadQSjGfz3n961/PC1/4Qp588kle97rXcfv2bfb29vjRH/3R7eT78z33vF+csWDdVm1Q\nuYoJmBJjKtI0c8c0uygltu6uovtU05RbCP3khHEyDCOfsZqmfvM04dZFnTANaYwxJdRTbM+iXkhl\nyq2KXrdIvZxFVZZmb4lrW3LMGCzKJ5QPNErRa8Uw9LgyUY4+4qrCSuXMiTYpJbbyKZWLyqL8XcUR\nlksVn6IcAmLhWdi6YrQaYtru6NNiOuEF79T7ynRcThPWaEI5WQj3QJQQKucixfNE7xmIsskoVRgE\n5cimNbrW4lMwGavATzHwPgCBndmM5lxNzoqD+T5d77nn/F1opTg5vkXses7vHnBh7xyHe5eZL5ay\n6SiNdY5hGLbDpRiSyBGDSJG2OMmxQ6UklWCSCJucMzOlcVEW3eH0hLqq0V6uiIm8ZyqLThWmwOB3\nZhWnm44cAk89+xmuXPk0i3qNMYGUYmkHKE7XHdG0fNU3fhu/96H3sll3jMOK/YN9Dg4OeckjB4R0\nXeh1uwlnZ9x/+T5QI1UDbVMRo+NFj+zijGY+axmHAWO0GBrGFU3lOD09oZ21orENEXJmvliyPl0R\nc8aUwa8PMv/Y27+LTbdmubdL1/ecnJ4wbxPGQAgdkPEjGFqefuoKh4c7PP3MDVwlleTQB4Yxk4LC\nB8X7P/BvOTk55Stf9qWcOzrkm4Ffe/d7uHTXPifHV/md93+EP/eKr8DZBf/HW3+RV7/mGzl/8YDf\n+n8/zrue+A2+/KUX+fb/6q/xa7/6dh56aI8HH34ROcOv/9onuCtbmnbBub0L/Ov3X8HHhsOLl1lE\nzbmdfR7/1d/mOsfctb9k0bZ88slP8cD997G7J6wOW9yyKseCLXVn1Vm5f2JKhYSX8SGQc9gqYKYK\nLYRQdNuTFDEya/8YF+E3velNLBbik/6VX/kVfuiHfoif//mf541vfCPf9V3fxatf/Wre8Y538IY3\nvIG3vOUtAJ/3uef7iCEwdP22sop3SIlUztv0gukxgWOmmJ6tv99MWL1Jv5mL/lUqzGlBmgY9U+Dl\nNPm+c2AVfcQ60SwD0si3dturNWXB7FJgNmtRzhEBnRRD7+munxI2kcPDxNhkjrvb7B4cyIdh3fY1\nnz0Kiau0beX3lOl8ka0prYhl89DWYJXDWQ3Bk2PAp4L2LBXsVpNaTgiTOuMMPSigo7qWKXgMAQ2E\n0h/X2hL8gK2F+Wq1xftJTSG+/pzEVdW2FVlJuq5RMrRSJWE6Bc98MZP+t9Z0657DnT1WpuNod19S\nMC5fxKjIsmkkHSRk5rMlfd+DUuzMW07CWHTUiSH2WNviZqJnHoaRcehoZ7Oi8Q1AxFZt2QQy6/UJ\n8/mMujIYkXuzWq3ls7AWHT2V0dTOMPjA2J0wcxprGpoXXGbRJobTj+OM9NvHUWR/h3tLbq9HAO67\ntCSxxI8tly5dQik5yWy6XiA5uzMWi6X0gf0JqBFrI13XMY49KWaunw6MQ6DvR4Z+Q785JqfE6ekx\ndStV8a1bt6hcw7NXr7Nabbhw4Tx1W8JOtS3wHBkI/9pvvJeHH36QRx96mKwSOXtCFEuxNprkI//n\nP/8lvvf7vpODoz1CGrfwpZgtpJrVKvHpt/8rTk42/MVXXuLi5XMAzJdH3HXPvQzDVXZ2L3L58t1U\nbsbVqz333XcfmZH1asMjD1/iE5+8wv/99nex7nqWe0c0811I8MyNDcudXaxrWfeekDPYioRFGUuM\njo9/5phv/LIvZ3/Hcu3qVQ73D/jQRz7K/S+4h4cevp/KKbTJ5OzJXqOyfc6pb7rHt7yQEcZRIF9y\nK0+nVFfWjUiMoeQX/jH2hKcFGOD09FRcMjdv8uEPf5hXvepVALz61a/mR37kR7h16xY559/3uf39\n/ef94kIQ9OMZ6/XOHUoRvOxg1grtaaparam2i6bUdbFUx2dHesnHUmKmUIKOnKhnOWeICavl+DZ9\nLaWEmwhtU09aCWJPeMaRMHpQ4GaNhHDGSEJhPBiv8Lc7xuOB4+NEOqjxumejVgDkKJpiraehoqQ4\ny6BwqoLlH1t3FIIVNFqTyIx+lMDJpGmMYBWNmv4GAbhMjNSqknDE6TFxjrVy+FFvj2FTS2iqmmMs\nlXiI2Ep+rlFgFHTrFW3bkkr0zdj3IvFR4JxoWeuZyOXqqoFs6NcDWlmausX7QOMqZvMZ86bBDx1b\neHZMOG0Jg8BfAPrTFTolrFGkMDJvKpJKrIa1TO8rQ2VrIhFTyaDT6Yqh2HCNURhl2PiilDAJHwO2\nEd5uIrJZneBcxZiSQH+yDDWtUix2dtF4nlp/SkhjlaOuBIOYVOTQyCnn6mc/zr/70Id46OF7uHn9\nU7hKQ44E70lJEyLM2j1+4Rffxcu/+ss4PFyCkiHfpN5o6hkhKt71rnfz0pe+jHvuvVfcgEaBziTg\nvgcrhj7ysU+8m4cf/WoeeME5yINs3MqgjcPHTEKzOvHcc88jGNdisvSRq0oMLkZbojGMocXWNYmI\ntTUJ0VlrEklD33tgzvHpCW3TkgrLxFYWtOEzT19hsTtjYozM5uX6q+D28TFHRwd8x197JW/+X97B\nxz9+jVe++hu4ceuE+WyHTQ9ZO5FShszNm8e0sx1CyngyfhgYPOyfO8cDd+9z/twRH/+9T+DqhqvX\nbtCNA48+8gCzmaEfNrRuVtC1hdyy1WlPJpxMVVmsbfHBbwH9kv7sy92W0UZR6Zq2hC38UR/Puyf8\n+te/nt/8zd8E4Gd/9me5cuUKFy5c2O4UWmvOnz/P1atXSSn9vs997iJ8cnLCycnJc75mjOHSpUsY\npUqwZiBNVS0l+ThnNBqMKdpZinf/DoZBVkW/WETcFAvQtj0BU8siA7ZQqabv/9yK+s7WBpTFW59Z\nXfXUqzaahCL0AVX6knGMqFGjh4ztEmYIVLstnTIMRbI19AO2USRUWXwTdgK05zNSWcrPdXflMuDT\nWlE7Yfn6oWMdPP3tY6yPOGMg5EKYu4MXUY7c6o6vhwI50bpwb4tZxRc4dmUrfKmUh7GXCrXvt4v1\nBGxRSqyn2mk2mw3i+VMUMQiuaoSbpBzGOpFcVZYxeGIOBTYuGEpnZLOTOcE0oJSEBW001liSKvbh\nwiMQJq9I1FJO8lkouRLGKDFDtTVoZ0kxMsZAJG83e6XUNgHaakNETlp99LTzGmsVfR+YNTvMdy/T\nd9dIjDiXSGhSCMwXUjnu7sz5nfe9jwcfvou7777IenWLuhIbcIiw2UTado+bN29gjGN3uYM2mZQ8\naLF/jz5jdM16NeKqGcbOUCYWPWuUHJio8Ulzsgnoaom2MwzCLckFLiVCN03fdTLYjmCdgizo05g1\nWRlun27k/tIGrcUrJshq+WwMcnqprMIifeyyN1K3LVnB8ckJF87dQ1JZtPiVxjlNiD0+BrSzLJYz\nvu3b/hI/+RNv5iMf/RjfeO/LuH7rFOUUi8Ucg8YaxXodaffKe9Z5kYlGqOoK1zj27D4PP/Iov/7r\n72G5XFLVDR/+3Y9x113nODq3xzgEKg1wBpw/o9lNZ9lyPWG39/x0n0z3Ws4ZkkJbOUFeuXLlOdhS\ngJ2dHXZ2np+tWeXP5eX9AY93vOMdvPOd7+S1r30tP/iDP8g73/nO7XOvetWr+LEf+zFSSrzuda/j\n8ccf//8999hjjz3n5/3ET/wEP/mTP/mcr91111088cQTf5iX9aePP3386eNPH/9ZHq94xSt46qmn\nnvO17//+7+cHfuAHntf3/6HVEa95zWt4wxvewKVLl3jmmWe2O0VKiWeffZaLFy+Sc/59n/vcx3d/\n93fzrd/6rc/52rTr/M4//Uf0x7e2X38ulUqqs1R6vHfuVlN1J9Ue28gYVVQRWQEl7DNPlWwGdwcZ\n7XOxhtPvNk6q3DMX1fSaSo1eWhsKRW8zQ2uZ7+1TR4u9Hdg8eRN3HGlyjX7Bgv6cIznLg3/hm3n/\nE7/MztER7Wwu2Xc+SUJvkaFtzQHlNyprSq88obOA6kOMxOjp+zV5HDl59hpsemZK0xoJK524yFuB\nenHObf/mFAosRZU0aqnEJL5J+LTejySVCclv3/fpJJCmqXOJrEdJ79xYJwoNrXngL7yST/7SL9HU\nLZv1RmBBSkwP2eRJIEIegzAztBVdtxKzzDiMxRouBo26ruU0pDUpQVPNiCmKXVfprRkjpUhTiwty\nHNZbdcv0PkxAn63RQhj+6MKiUAULKc7HgNKCJA1OoW3C6I4UbhL8CdYErFY88PK/y7/+f/4B3/sD\nb+AH/vu/zqWLR8T+GOekqh/6iFILel/xw3//H/P3/s53c3TYoHQi5FFOQihCsji35B/8/X/E3/5b\nf5O7jhxKjWQiMXliVsRsUarlp/+nN/Pt3/FdHBw0KLUCdHH/F741ih/90Tfzg//D94hQKG0wOpXB\nckNWMz707z/Jr777ffzNv/1XSWqDzQoTLCmfiiuNJe99z+/xwX/zcT768U/zY//4v2OIV/n273kr\n//R//BvsH9T8k59+C3/+G76JRx69yKc/8RTvfc+H+K//m7+IsiM//uO/xNd+wz28+Esew49z3vQP\n/2f+1vd+N//sLf8rL37xozzxLz/CG9/4Nzi32GA3nn/8E7/II1/zCF/7ikdpfebGp3t+/J/8c/7b\nH/oOLu5U5JCpqyVvfMNP88pv+RounrvA1Wc+w/7enJRHahO5dO6AnZ2d5/BCPvchjKq8bcdN/3uG\nN5X2T7M84i99z5v5uZ/7uf9oJfx8H3/gIrzZbDg5OdkuoE888QR7e3scHBzw2GOP8fjjj/Oa17yG\nxx9/nBe96EXbdsMLX/jC3/e5z32xn+8FT4DqaVG9U7srqoC8bTmkJKzWnApnouT4THBumWxNkrQM\n2aPipKZQZAEliB64UP1lIZqO/gIDnxbBmCbAihyljXOgBLKjYiakyPp0pAsrsm1os4XKMBgPaaTW\nsJgt0AWJF/zIanUsNl2tUSRUEotkDhBVFCShUkSy9LeAMQRU8NgEMUfWmzWbfo0q0rLaOqx2OG3p\nhw5jDXVVERGNaSh/rzaGYegF7h38mQywqDSKM3rbbkAJjcuHuIUQWV2dLb4VGCswfFsmy1EV1Qui\nevajKBucs+TgCUEidhQwbvrt559iLqjEiDWxmCkclW1Q2WOUQ1J/BZqUswIM7WyBMUYGvEiidvRJ\nzCRK+LzT73eu2jrecpDNylUVocwNYsyEccBUFU1VkRGSmMpJIuHjSEoZZ44gOVLqtzlsu/uXefnL\nvwxrHDn3pW0xp98ElHIomzE50w9KSGu6WJ6VJANLsIArcUEa6zQhjpDHEsI5SbEyCkFZKiSIFAXG\niFlBF618jAo/SoBBSpFKFQ5C1gIaSpFu6KhqQcIarQWYk0Z0rojBo0xg1a+gsugatFUMK1mojNVk\nX7G66Zm1GmM1qy6y2J0DHqUcmzGwu1zSasdmEMT+vfee569/+zfztrc9QdtK/zkrx6AVQStmrcIE\nmQ1sOo+rjYC7iEQN3WDpR9g7OOCuu8/hrOfZa0/RtvugDU9dO8Enxc5iJgogkASanFA5YoCUBTeQ\ni0wxlws/IEl2+AAAIABJREFUl2tWONW5BMLCpUuX/qBl9PM+/sBFuOs6Xvva1xZ/uGZvb4+f+Zmf\nAeCHf/iHed3rXsdP/dRPsbu7y5ve9Kbt932+5573i7MWVSb3d+r6pgFRSgH0pBUQiIrEnwRi9Gcm\njDEVH37Y9vtkaFMhJqckN4upoAi0BUVoSpVkmODaVgseT1uLUhHRHDtSDhjbSJBh6c0Z45hlw3Ac\nOGVFlzWLhSgOxs3AmHosEgAKcO7cEZ+68hQpBg729lnUDVZZiJkQE54Mthg9lCJ6UTkE78nrjni6\nwhM5HTYMybNoWpQyKKuxrmH0QYZKGCQQNeCD/C0oabmGmLGVxSizRVmCbDgBoYahMsoacgw41+Ds\nJPWRk0FMiuALMzlp+kJaAzEVTBWF5PJptCmD05SwSmOT9F6nwYeEsEt+Xtu0UtUmifnJSQZ8WS4S\nuW6cI2sjao4s0j/MVOXLHlxVNT6OuEp4w+XPlM0+JpIqQ0rt0FEAR5sg0UAVMPTd9uSgtSYPI3Wl\nyblGpQqNpWk1KcngcxwsL37RS3FoYjylqQsaVbeS++ZPUXpOXRU1T1HCVNpKH9fU5BKyOY4KbSWn\nLWXFFEZAhhQlF3AYs0xKc0ZFOVWYcgKM3jEOkZQqMgZtwWYlP0uXFPOUWXcbZnMlVl2VUGbAK09j\n9vGhQ+nI7c0xZlmzOGfIKtEdy/vYti2NPmB9I7KYCwjneJWo5zVaZ4ZOsRoyhzt7zHXNZ9c9zcKi\n1IpH77nIt/y5l/PP/q938ZHf/RB/9mtfwvFm5NgrFgvDQhmsMnRBk22NIqBSQinLpq856aCaOYzt\nOX+0ZGfnMu95zye49IIHuXD5kA/+h9/hwXvOc/fdl1h1A7qqMCphw4DRiohs4MrocsLMKCOEupQV\nSjLFSF8YpvsfvAgfHh7y1re+9T/63AMPPMDb3va2P/Rzz/eRcpKqtCgdtoOvqWFuDNlul+CtSy5P\nBOuSeqCtRRtZhJUTf3nOmTFLpWmsoaqccIq3qRZnSRbTBpDLBNB7T6U1Thvx+StxUgXvtzZiazRo\nQ60VShtSTITR0/c9TUns6PoOs16xW4Y3yhjuvfdejk9OePbaNfrZnL3dPaJ2ZI1UwSmgxigVTYhY\nD/QjeT2QVj0pCe6vHwdOuoGd5RK3aBhDJqlIXTuSytxenzCbz0RnrTLWOTabDdW8Fkdh8JK4W4sS\nJEVIJcrGFNJb8pohFxddYXfouhb+sRISVT1r0V2Bkpfw0e3AtpKqWZcThKlrXFXYDVEAvsFLu8PV\nFQyezSDsC2V1qTJLWKXWIp1LCSFMyIJqlWhbVS4MjtIqAp7TRplOWU0jn8UEL5o2/ek6cE7Y1NM1\nqQoGdExjuS4npY2l2/TM5qIEWcwP+KqX/VmeufERnLtNjhtiyrJ+luFnjAlRPsnQ+GzAfNb2Es7B\nGchJG40Pg/wsfWZESElSVFTRNU6a8VToe5vNBldJ2EFVGUyIkPS2VZVzZr1esbOzUz4vGWxbZ1HJ\n0DQtaBlMVlXD3t4ek8sUhG44jiPBU1ylcOP6LQ4OZuV0o4GRtm1RKFanq618sraWuqlYbeCXf/m9\n5LDma17+5xnGnnk7hzJoFH24RHfFmIgqcnp6jNZwdHSEMYFIYL6Yc/XpZ/nkUzf4iq9+lAcffIgb\nT3+aazdu8shjLyKEkp6jDd6PRDTKpoKgkQ1OW5HIphBFDhrB3YEq/aM8vqgdc2edg88RV09ptmXS\nueXxxijHUGWKik1g6Mo4ukI1a2atsBrUHZE2SnY/O+lxYZvqHEZfGK2O2lUEX1gPSaQqRin8IBpZ\nZyxKS7vCYPDRo1DUZcIcsiInTxgk6y7ExGeffoqTsePCy766hGpqlrM5p2Pg5NZt/OBJVU07X9C0\nrbjrYobRY6PG9z2ba7fYXL8pF0VtQWVqa1gNHe35htl8wa2bN7Ea+rGncpL6GYgl6qYFAxGRw3kv\nwJKYMyaXTa8EhipdqiWrIZfJubE46yRGRht8EEZyypnTTScbo0roomObjuhRZbAaU3SpOQSUtURf\nkj0A7Sy2qkAr4jAwWyxwrkIpWK/XGOfE4qqMgIlS+XxCxBUOsNVGnH0+lCO79HxzSdmoqmrbYplO\nPM8xrxQr9mw2E5j5ODKOov8N5bhva1s2mFhQpIYcFVbLIqyZc+H8Pdy8/SRT9JU1DpUtIUv6d9/7\nsoiXEFldUKaICkcIbAPCMHbkPG4lVErJJq4wDJuBYRCIUAgBo2Q2sm37RIHvt40jZ7kO3eTdveNx\nehq4fHmfKW4+xChw+yQW6JQSXZdY7DUsl7sl5UU2C60Vo/eEMCWHK65du8Z9L3isSEHlvcplEz89\nPaZpWjabDYtmxunpKZcuNfyVb/k6/uW/+A2GscaPmWa+KJUqdL2oikS3n4gZ0UlXMJvPyOkYbUUW\nebwaUdWCw4NDPvvZJzm3t4cxhg988EM88NAj7O3M6de32C3rQ/AjIYs80zorG7kWu2pMWXTV+o9R\nJ/yf7SHlzHP+G5NEaItO05BRJC8uoZwiykgPUmkR4jBpe6tKgOlKk7J8jE1VFbDLQPBjcdHdkUNG\nmc1YS13XW03hNLwZh5GsNDkrmrqWGziKicQUx5ou8TMhRFL0kmhgM6HzLGpD8B3dsfBab3z2CjEl\n5jtL9uoZfVJ0pxu8GcijJ48ShGjRqAwnp6eMpx392GEqQ2MbTocNGz+gas3Fo3PszZdkH4j9iJu4\nrMHjrGXoZDDVNHI8dVYTwigZYE7wiGPRCYt+WI6qpryPgYwqsjOVxdSxXMyZtQLBWa83YqnO0jYQ\nbXEi+hInlQ3WyPenkMhBTiXGajZjJ+hRVbjEvSfHiWMkn00YIrXTECNp4nkkQIlmWikFBcQkV4P0\neafTSlXXjMHjtAw+nS7BpfFsuBtjJMezOKIJL2kLajLkojdlchtO/A/DOHaIOQQqu4tzEa1aQpJM\nuOlvV4BxlmFcY63CVQ5jgjj9kGw4ye1TW912XVdUFmLyWCMnibAFuneEYGiaBpU6Af0Xp6Aycg9t\nNhuaxm57/BEjqSlFwinVcpZkEy0grRwjYvtV+DHiU6DvPefaGXWzRyaLiQaRq637DTGxpbHdunWL\n2Uwq2Zwhq8hsNkOpzGq9EuOOVQzDQLcZyUnzki95CXvzin/4Y7/A7tKIVllpYSePIyiDNQqnKlKC\n4+ObtK1Y5ofNhmW7S8yabsicO1xw6dIFLh8u+cwnPkYzq5nN9/nkk09z/vwB9186x3p9A+dkHpNy\nFDddTJA1SckJbDpdfGHq4C/yRdjnhI9ehmUpUVc1EVEFuKqS27/YDsVeWpWpfEQlWSylzytMiBzF\nauvQcuwNmaHvIWecsiSyDGqKIUTYxVkqihQFi+mcsFr7Xqo3EnUzJ6RIDBFX15AzvR/ldWdQSTSt\nADEJ3SlbOWrvNjN04cGmkw1+HDk52RDaVhbxYrNOY8SZGqcMvR/QxjKqRJpZqnaHNHgiUMeKxhqW\ny5nwHbqezckxc2XRPjOrZ8ToBfpSi6mlX/WCKWwakk7MqnZ7hJ+O7H3fkykVRk6QFYvdXUYfioVc\ns5wtCL1sFAZN4yQi3Wixg1trpQVTCginNLqA11OW45/vesiSJTihO1PKmKxEnRBlIBtCYKdZys1n\nYkkvkdZK0hlPIoVIVZyPwyh9aV1A6VVTSxqJViQytpry4xQpBvwoMUvaWqKXYfAEwbfOkQunmAL5\n8dlDyfUTe6zC6AqjxawxDJFx9MzaPYZ4jE8DKUndWNUVPq+hMEkqVzGOG1RdY8vCq5W4EzfrNVpP\nWvlpQzoD03sf6PuelEqytLZoJSqOEAN+9BhVsTo9ZTaTvn1ImaxLtJKaNO+GcRxYLpdilrCakKTt\nZ7SmamZ0Y0/OiRQ1Fy9fQqHpe9l0YgwCTzdQNw2guH17g6ucDDhDomla6tqhky6VsER5GaSCPzis\nUDnx0H338Xf/zl/hJ3/67Tz56c9ysPcQlTZsup6qMjhbkb2EKhwfHzObz6WKdRYfIj5GNmNm72Cn\nXGeZC5cucXx7zegVy90j1t3I+z74QV780L0gxEqadsYwdKik8ClIuoapcEqXNecLs859US/CSSuS\nlcrLZBgK23PyXKSY0LHkbymFLWa6nDIxZ5RKhb0rvUBKNZRSxvceSvWkSyUUNUQl/xaqqCW0JhUr\nMgbW4yAtjspinQOkvzv1OrNWRC8X7fawosEoycWrXIXKmjCOVMFSNw26GDp2qha33JPJv9ay8GUI\nmw6Nx4TIrKpp5jOS1jQsGEOQ5IUQsEqA8E3doKPHr3v8OBJHz8zWiCghUzlZZLMAPpnPltJCMYZU\nIpZUynjvUUY2sNlitxg3zir7MQggXwwYkhldu6ZYqMXqKUSsEVVuckFFyobkfRmSlvgesZgbOdWI\nv3zbXzfWlRaRkfzkmEvrxEs/2nuSKQGUKmNqRzaavliTlTWCMU25WKo161V3R6JI3gLsrXOM3jN6\nj7Nu2x+O8WyxOwPAlwrYTnek2vYptXZYI4PlytWgDM7M6bykQE9OR60yYQwMw4gxoq7xvswmtLQi\nrJF8QO8D87ks3NlLbP3EU1FlmNj3PU1TIr6CB5MwReGQi5RyHEfmi0r6xCj8OFLVrsj4EjF5xnGg\nqqrt8NFgYBsAq0gJUhIJoED4M8OQyvsT8N4znwtAK3lPjImmalFo1qsNdV1LHqOuhO1c19sh4zAO\nzGctpIhzisW8QWV44onfZnfuePELH8OPicPDQ1JShYthWK1OcVUtBb0G7yObccSHjHMarSPawmw+\nR+maf/Hu93P3/fdy113nqJo5n/j0Z7lw/jw7OwtW3Ya6bumGlZzispdg4KlVlf4E9ITreUtK47an\nNNG/Ru/JRpO0TC0VGaUNTEkExVWWsoQrVspCTNvj1kQ6IyusK3lfGrIVUHZOJTrcyGqvtSLpMq42\nClOcMmMKKG2pbIVranyURUlXrthRJXlZxSyVu7ZkXTLBrJIK2MrrBpFDpZxKnDnYpkblzNFsLke/\nrme8dRPd1jQ7O+jKQukd6tZhqwqVRUmxOV0TNz1aZXRTo6salUXCJgMxT4wiJRsoETvaMMSISlL9\nx+zAOIyW1kNEcviMNmRdAEZGiyJFrG/0RQUoPfdUnG3yH1/gJ1MFMRb4ftIi7xu9sC4kYdmwWq+3\nuXCyiWrC4HHGQu22x0EfI0kJrS0qSedWOaKM9FHJmdrKaYMYxbmnDXXbCKIzSdKKBKhWbLrNlies\ntCKM8pxPscjlCtDIGEL5vLQS6IQxlil1mXy2WGutQFl2dg452VjpC6szazhKonbaVsIGUkpCYzOi\nZAlhQGvHMPQsFkXTrIu1erLnR6GFbTYblsspkFZs5SHIkCMmhVGS4LG7s7PVtk+6d+mDT/FN43ZY\nNo4jCXFe5kTJ1MsE3zGOnr3dQ7wfWa1EDaI0bDZrlssWrTWbrmMcVekvK67fuMF8NicET3KWzaZj\nuWzK9RJZr3v29/dwBogjJ8fHnDvc5Wte/ghvfduv81/+5UQ/BFIURC1Ij/30dMVsNpeTC6KK8p2k\nrbSzBrRc3yhIKD7y0RVdfpaDwz1mtWN/ueQjH/s0d911mQvnjzhZ32YxXxDDSAqeFAaUmtxzfwIW\n4dneDlUzYek0tlSMfddTPi1yLFCNEBhzJiWNwkFJqwWIxhC8XIhay8KrkN0sTbbiFORGKotvQm2H\ngrnodUkS5e4LOU2VBSLojB976U/HiHMVurIYoHKOEKK0SgosPClF1obsHF2GSryUbAiiq8WAMVRN\nTfAeTyZES7KKGEfCauS078TqmxNOO6JRqLaClIjjCDFQNY66nmO0lpDSEMk+oIzB5IwG6qYm9D1V\n21A3DZUXAhhaY60T4I5ShChGhonhITbmkRSjHM+zJH6EEDHabauFGD1Wn1WPIXjGUbQ9zYUDUsoS\nua4UIJVZADofSIuaKaYtZ5G81vUcXzLT1ps1ZBmYqVrSrQek/2OrEv6oKekVhZWcZUH3RTbXj8N2\n+BKCRAZZ53BVxTiOnK5WLFthp9xp857ki2EYiSlS2QpygS0Rt33iYZQeaYgjWiuWy33yFSO91QQh\nC+vXVQ4/etq2gSytm4lXMMnmBBsa2dtbiqwyezKBVBK7U1ZUrmYYBmazjA9eBrkZYoHNx5Qw2rFa\nRc6dE9NCyhmjJHMu5EjSDusqhkGqVfBbXfjUQiGDcw3GWtbrExaLOU5num4NUE5yG/b2dqibhpOx\nJyVKL91xenLCwcGBnGhTpOsjl5ZzwY/GwGaz4e4HH0TriMmJMHY0bcN/8U3fxN5yxtvf8RukoeUr\nX/5ylKqoXSQZzep0zdHFS6QcMVpTmwatexKJ2XxOxkvwa9JonbFV5tLly9z/0AN8/KMfIMeeBx/9\nUp588pPcPt3wwP13Sf5daVEKwiWW4fsX5vFFvQjfvHkT368lhh1oavGN+3GUm8BaVO3QtcOUPpkI\n00tibfBSQQU5hKEmPWsqvcYkEBlFAboIOGcK1EwplUrYyppfsttCjGgrpLXRx22ibTObCSw+ZomX\nMVZ6j10vx9WUadtFiVmJROcIWaDjALPDPTkOV466bljsLumHgZQzYwhUdYN1tvQW5yQvC2llHSOp\nVJYio9JKtL22MoQUMVb+Bh2RIViWalcbTVskecZZqjJMzJwlVmutsWVY473Hta0sRDkRYijvmRJX\nXZBx3UR+AzAFjTklXEzKgsPLl4sMLTAGXwaApnw+hW6nCyciS+VhstmGZfa9ZLGN4yAg78nYkyN1\nLaCfzXqNQlGVDLdcpGj9piP1EVs5yeLL8r6PMWCdZRgGYs7M5nOckc0ne6HDFcECAM46aluTjS1q\nnrxdiOvabQdVgkd0OFMDGldVkCQ1JWvJYvIhMJ/PSyVdSU9eS0VbtTUhCRVuNpvJZxxEnmdKbFPK\nosoYhpH9/SVNU5NDRCNcYJctMTtiMIQAi+VSOAnZoJNwNozKxGxQxmFMZLFYEMcVKA9a2BVWO5SW\nVPKmntP3AsbXWrNaCQcmp4T3id2DXWJM+EGui6apsEZzfLJid3dPNr8x4kNiZ7HAFWVJP3r29nYF\nUITi5PYxh0cHeO/5ypd9BTom3va//xZj8JDkWvURNl1iPp8Bol5KOTEMAtVqZrUEgFq5N7pNTybR\nzhqM1Tz48IPcuPI0125c59z5y9y4/gy/93tPct/dRygrOR1aZcixZEv+CaiETUp06w3ZaOqmYRxP\nicVaHJXCmIok9qhCRRNCknV228Ko6hpTu+1QpaolnjtFSry8gF7aZSTkKIuKspL1phQxTem2Gqun\naHMZ3kkf2BJDQpVjudxAhjEMW6nTvCgLJrefQtMCpmrIym6PrLvnDlCIOwqgH3pA0kMaY7FY0phR\nQyKkTo7BGcacRa5XSGvZuZJBFhmTXPyT3Mn3PVY7+f/kYie2RRYl/ThraxSm9NnEfnxmE5dFLAbp\nHTsrwBytJFHZhyAusxC336+1tJJiFDmXQsEFGG4ci9pDGzbjhl6JbXY66uvy++pK9MMpQOwFtRj6\njjyMpBhQIWBR0hOfKpascPN5WcDzlhBnjGHoB3wMeBIHFy8QU+L4+JigFG3b0g8D3aqgDbXFdwGb\ni5ohIwqDGIleKsQxeGjlM6hcJXQ5a1itNkwzdCkMZFBrTOb6zc+yu3NEzjXDkKg09H3i6PBIfkeK\nNI2j6zpqYzDOkJNmterFhJMyOVmkKpNFXGHJuWZzCvv7h5A8tnIEHyBknHIYajo0J+vI/rlz0nPP\nGlMZOSnahpwNg08l9yBhrSYGg80VTtUENdD7hOGIyu1z4/hZahvQtuZWgVGlk571Sc/R/UfkWJP6\nOdHAvB6wWG4cr7n4ggvUdsbpGFj1gWVlaJPmNFluhsi53SWeDhsct54+pTlYYNWASj1f/2Vfzi88\n/tv81r/5V7z0RRd49JEFsVvSVC1aGeoq0LhIGAa0nrPpI3t7O7i8oVKRtbJUecEQYH9f4fQp2fS8\n4OI5VqdX+fBHrnF08QFON0/zwQ99kMce+DMcLOYkdUIygSEkmvgnQKKmyVRGcI7jZlNQiHp7NCIr\nXJmi5+CL2Ec0wFnJTR/6jjHKgEkZCfgLMaC1SIxMSUtIWm7WlNNzBPo5naXnaqNFC6nO2BVaG4yp\nCpdY+ssoVfScaitnQRU77Nm4DsnjkgXx4GG4dfVZBJsnelY5/hVyVZFVTYaHGAWibm3RJudUAkMk\nDPVOjkOaKv/yPZF+W2UCDHGDK4kkWmuCjvgxsAXkZ8hGKtRUrNKVlv5sMqZQ4yAPEpC6Gm6irRFO\n8SRrLovWGXQfVjduFtmTJhpIRtQFU6tJaVnsTu5MTfHgR1GeTPrwsF3sSwxVjmSVqeoaa+QUlYvZ\nJ8ZY1AOJnb09xmGkbRouXLhQWghS/R4cHhBTkhbO6Bn7gX6zwY+eWduiVIWPgd57fEjUWsIlp4o/\nZ3vW74Wt2Sh64W9470nRY3SDUhpyIoREVddSbZO2Q79Y8hWnTbNpGnKMgu8surxMQmVDypHBB5rG\nkujF+EJAl/68ygHrGrL2VI0G5dGMoCzaGfk5WkhnxkJixOrIZLtLMRDzmoSlXxXjjBpJumcMCh8l\nUmp3vuTkZMOFuw4w2tGtPYsFGOUJIXDt5k0e/pIXy7VtHEOIHO7vkMeAdUtO+469xQJbJQwV4xBo\nL9W4yqCGgFMwhpFH/sxjvOXNb+c7v/NLefSxb2K1Hjg4WhBSIKQBa2YMfcBomLUVTZOwKtLYlnWU\noXZVOyoH2jkWoaWtDviYTrz/fR/mhS+5zPnL93L16m26duT8JbH7YzTpCyRS++JehEv/NuUzAIug\nI5HpTlFDyBS0aIedFfRlPJu2W21K5VJi7qumHB0VOQdZWJPCKVOa/Ap8ukP+gwyZtJ6iA0Fn8dIr\nOcpmBZPLDqVQmpK4cWYKiZwtjttUY5WFIg74VVcW+jKNnhKhS/UYRg9a4UwtuXNZYVJCZUUiYbRF\nI6kL21iXKOkW1hiw5oz7kDM6TxW9cJGnU7bOkjwh9nDpf1XWECJiyCguWVV+zjanq7jcKidDO03p\nE8eA1brgKs+myk5rQkpURZMcg8CHJl1yDgltHCEH4ig5dhFdPr+iHDCGMZc20qQpLi9Q+cjYj7Ko\nFdVDSok4jDjnGE5WxJwYq0o0v2VAhtbM2lYkTimwVgG3qKlnFXUo0VZGs1NX1FEkYXaUHD0y28/c\nGIPP0nqR3EMZHM/aJXXVyOekPVYrctaMQ2C5nCEOQEvOSeRXagoccPS958JF2SBIG5QOaBu3WuFx\nGBnHgQuX9wQAlHo0GaWT3EdKyXsaVjStI+URpTwQUbrI9HRNBup2BmpE6QjBl9mAJ6kRZRW3rj3L\nfGdGuB6IecAZw+ClJ1zVFX2/YT6/R4qiHFguFcZIT/j6jZssl61wTGzFOHr29w/kfXKavl+zXM4x\npkOhuX18m7vnD5DiiCmbjveJb/2rr+TJ+36Ld/zCe/m64wUhJpltVC0qDRhVcXzrOlrBbFZjTWDs\nO1QDXTegVC4tINmATLZYt8/vfvQD/O6TN7n74fNEIhcOL7O6dcxnnrrJwcUDNJHAGYv7j/L4ol6E\nXV1hlUw6U6kMc0oMw1gmygLTiEmALiEGulEYF01do7RmGDuqyuGsIiOVQhhi8eeb0kO2GKUx6Y7K\nFcrkWI7E06LjijV62xQskp08/ftSb0hGXalAtRFTQl1VWxdWionauK3VFMCkTGUNqTi5VJRKqqor\nWbgMKJXJyWN0ARXlIK4qpchJs0W9l6perLWZgHjg2VbApYqK5b3gDtmVBmXkvdFGFu5hHLdTfK0N\n2mq5qae+cKmUQ04yUc+JbuzRSvTHSilC9tJqKe9vKuaJmBPZyyClchU+BNbHJ1RVhbEyRNU5E0dP\nVBrXtsJ78IFKqUJQS2URFyCNqUShkIIwhrMWueJkcY4+iKuuqtAxE8OwtSL7GBjXG1F9aOhJqLrB\naY3Ostn6cWRYbVCVZd7UOOPoM9tBps7CXQllCDl9xuPoadwclSu6bo3KClvVpGxkEd45lAit6QSj\nJIA2pEDlHMMY2Ns7kKvNWqAEUOLJKNANo/fs7e+jjCb4iFXymeYgyKuEoh/XuLoi57FIMmXjEqce\nbFanLBY7BXqv8SqhTdlgtMYZy8nJDWbzlpRkoGpKFNZ09wzDht3dJSGK3G2+MMWWDpu1/G2ZQEqS\nU+hchdaB9TCQs7goy67IenXKweEO2iissqxvr0gJXB14+dd9GWM65R2Pv5t+hNl8QbcZWdaOGBW3\nbt6ibsBZOTWlEEjjyHq9wQfxE4QQ8NnTDZLSstpEsta85KUv5cMf/g16H9jZO+KpqzfZpNvs77XM\nnz8o7fM+vqgX4U23xmbpu/V9L31axEV6Js5PGKeZLZZbV1PXdQzDQOMMu3s7GGTyG6MsEE3bstnI\ntDaXfqrWk3zmrIqRyqoscGXlGAVnBqjis0+gy0KhzjLuQsrUTYMp8dsxJcY7YlSmLDdyKEdKUJUm\nGQnPNFYWERWVBIISsbUVXWc5NqNLL7jIjCToNuMqS8rgfZL3p/QUAUKUo5Roe6W3qyiVcmmzGFv6\nwGSqyhFLaycrGeBlnYkEbG0I3hNUORobGVxu0oA1FlPLhH8TetGKJjn6TYnMXRylf+8Mo0qMJeHZ\nGkt9sCOtg1F0qHVTbQerKy8bra2cLJJjXwaISuKdtFS1MUVcW4kkMGbGKBlr1ayVQW7MRZoWC3Mk\nCmnMStsq50StHVUyxFORYumsqCqHVRpnNWkc6E82bJQ4OeezOXVdo1KmbVu60iOFcnhzButm5GgA\nj9YDVllGLCEo6kZuSa2tpKwYgwqJcYxonfE+Mp8vQDti6FHKiNEiG6AiBUM/ZJp6SYqGEGTDVEg/\nOiuLQireulpCXsu1jiEk0Zkb0zL0x7TNEqNqtIqoXKFUjVaZbBLaNQxjJGWD1g3tbMlmtYFc2BvG\ncXJTRzJXAAAgAElEQVR6yoULF4gnp1y/cYv5XBI2VqenBK+oKtGcj6OnbaTqtwa64w3OOWIcYQwo\nb+i6KC2W7CHDjZu3aFpoWoXyA1/7dV+Fspd5y//2i5ycHLNYPEjY3IZR5ko7C43WMiCez2ask2iu\ntaLEeEHG4pLDp8St02N2dncJjDz24od5+mPXuPLsTR546GE+e/UZnr2xYr7c+4Ksc1/Ui3AOmdNT\nsfRaY+lOO+5Mk0iMqDJwSQWnOB21K+vIMTF0A21dEUusj0LRlQpTa0dVhj4YS9cLgKduGrHM3tFD\nVFBcelLxxSQ/C2PRurjttGUYR1xd4YrhIgO2rrZGAJCFbhwGsrZYY7dd4nomFZ0xRjLRYiQjcepa\nT3xkuVF06S+Pw0BKmXo2L5rSkp0VE5VqxKKrzzL2WluhrbwWMTqcgWmcc4zDgDbCWm5bMXX0XU8o\nPIiqFtxjjBL2qZSinrX0fb8dylln8cVRFvuOWTsr4JzCG9hI33Dn6ECoczGilzO898xmM2Ez5IwP\nmfn+IX3X441sFLkfqNtadLzWEnxA124L5tFGF8ddK5+RUtTWMYwDTQE6ZTLzdratVrvSI7a1Y+x7\nKE5IUzmS0qgUSYC2luQDg/eiIMnSEssghhxtWK/XbDYb6tJ6mfrWSimGccDVBmcXBK9YzBzOBPy4\nJlLRrQeOzh2KFC2BQjZT62piDqSsWW96qnom7YrUkrPFaulOxmBQqqXbbJjPj4CGptLk9P+x9+7B\nt2ZlfednXd7L3vt3Pdc+fRoaaOQWI7dGoqiJTTRgTJAyGYkxGOMlkxBNKkVlKgqjNSlThTg1E3GM\nF2YyxihoMhBEYxAkUSQKkQYxIiCCAZpzuvuc33Vf3vddt/njWevd+7SIEjpy0pxV9avus2/v/VnP\n+j7f5/v1xCBONClV9L0ixgqjpwzdQKNkgd/UE+bLAa0rrl45YtruEYNlvuohWaIRPeiqntJ3mo9/\n/Dre1TR1RbeMLOaBRJZl9YG+OyXGwKSp6HvHubPn8D4SI3ivUDrhnWO18mTTFlKMHB0dsb1tszec\n0BFDhMnUQBJ9mOWix9oa5zt6d4pJMx59+yWaSvGmN72FaXvM05/yJGKoxHBVJbFeSoquWxFMxfXr\n15CFrbTrJyOiQEN0dL3n4qUpvTvCmwMu3bHHobH83od/l3MXL3N0qjhdPjwtczd1ENbRoELGG3OW\nJhWgjL8i3FGdNGEIDMvhhsJXEVh3qy4XZsQ6x4eeumnwaWBYDfLQKY238pCa1TIv7+OYpTZ1w0SB\nitIWnUjUbUNb1/go8EIIHmXEAVhbPbIIUOQCUcINIp8YlaJpakxpIgCqRjieyuYGkpSk/TME8HJk\nTV1j6zY7+kqg6vsej6iQbdoeASQMtRXKXYqy36VVu/BPnXNYY+hWK5wT26LgnGQiZEWsfkXfJ7wT\nQXcpJiWaSYufO5mw/IDWwtNNIaKQppdpIy7Z/TBgtWa7KIttTSFNGLoe1U6ooxcuZ4wYY/HBo1DM\ndraoaplIi0JaTEkkQFMaDVrHY46JGIQO55wUgtrJhJghLHHNDkTnadqW6daMlI+zD340bvXeM++W\n6FlL6Ae65YqQu+GiIvOb5b6coLCVdNeV/Vm7V0vbd902LJdLUtBM7DbEE9HBdZ522tJ1LuO0QVTf\nUoQopgQnx3O2d2pWqyXW1ly5+iBntmeE5KCXlVYI4tO3WPbEaDk8mhOio7GGIXhwIsLfpwajpxwe\nLghuSa+ETxziElQFOnD92ikp9ly9ckRjI9ZEPv7R/8pstkW1Mgxe84krB5w5c5kPfugK977nfdx2\n/gJ1PRmPNyURbQqdpx86tne38N6xXK5QWlHVhhAdx8cn0hGXeebz+Rxja7lflbhzJ0DrSIgJ7xSn\nxz3T6YSUIsH31GaCdx21iTz5CXfxb376ndi/YnnmU7+M3//YVS5evkgkSJt1SgQfOD4+oa5hMp3g\n/VKgyZycOJ+YTCpsFSB1GNNy8eIes+2K33zvh9g/dzt75x71sMS5mzsIJ40O0rVks4gJuSinlSKp\nRACCi7mzSpZdSQlCq/XaFTmlkMVdTBapzrxRkCWdUqiQdQqyuIHN9CvvPKl3dM6L7XuSIDCsOuac\nojPDIoRATHDKoaiDbbg8l4x0lPrTmoU6wXnpwrp0N1y//ypFpOXEWiku5g670vbca83CnuSmiDX+\n3PUOr07GiUfnVmsFG8wBTUrC0y3MiiIIs7x+RFVXaKVZ+qPMNZbGjE3PtUGLWaZ3nm4pLsY+epqm\nYXBSOJTfFcyvHwb62THWGplgEIhn+1GP57++/4NURuh7zliGENnb26OqKtpWdJePj4+JMTKZTATz\n9cO4opDCq8YNbiw4CusgkkJiMmnlgXIu095sLsAIiD90oo2gtGbVrfDes3/2jAjHW0vbTqiNxWlF\nPW1FPc5LB1lU0FYi4OO9R8VEv1yhlWYymWDy/Wf1mtIfguDeW9sXuOP2O7l+cBWdViyXjp/5/17D\nRz664Bd+4d9x9RPH6FzgCzmpWC4VqMjx0Zwf+qFX03VgclMIJtdLQmJru+X3P3LKq//vH2UytfTD\nkkorakTWM6mGWG3x4LXr/My/fi2zSUL7FZU22LohRA264oO/+1HOnTvLAw98gmkNbauxpmbZddhJ\nhQ+Ge+/9IF/6Z89w1+Nv4/X/9uf5ki96BjFJSFkuVkBLjJ7JtOXk+JgnfN7j8N6zWKzQSknThI4c\nn5ywv38WKVEkDo8OaNsJMXvYLBdLSCJkL6pyhgcfOKZtpgRBkgiu4/TgGirBX/yq5/Ooy+/gjW98\nGydHDYvOcfv+bVnAy1BhcUlzfHzMbCYu1WkQ84SqspzOI8kl9re3aawhxoQKAT94zu6e4/5PvIcP\nfeQ3OXPpMQ9LnLupg7AKCR3FDbfWUtQylRkV7X0MkAQW0LkXH1hTwjAYLdqlDimQaG2IShGdMBpQ\ngucardBBbnmrhDWQhkAiUJEpaUkReqFuWSNi7wmV1cAsg5dikw8Rq1Xm0KqMpSppOtEpuzEoUVyL\nYDNVrAqJpqlxkVHIvcIIQT6LhqQI2mv0IJoIWktBURtNMiJuo8fzIU0TMSZ0SlilcIOjQmALHbXQ\n9pynyTZDVVUxq1tc76gqTYgaHzN7IiZxobYVQwjsTLeFRqcNySVqDK736BBQ3guNzAesdegh0CLS\nn/IgwcQrQicda1Wl8Ysli0UPKGbTKd1qxcnpqfCOt7akoKZE2Uo0KQLW2HFis9aKg4cXtoDpAzop\nGtNkNohGC4guZA4XUL3UHNQgGtGnDx5QNzUBxYoTeuewTSP6FShSiNh8iyU10DY1TTPFVpolmr7v\npUEjN48ktYagQhDn7b5zeAcxOkiBO++4zJd/2Xmuvv7NPOdLvgiNE6U8pQRDthWoihgVP/EvX8u3\nfuu3CQfeDqS4QlVSXCZWVNUu3/+//x9853f9LxydPIA2CpUCNiZsUiRarh4t+NdveAt/++/8dXDH\npGGBTYEhIOLxyvL//vi/4gu+4Enc/Yyno5LD6sB8vmA622XlD8FMee9/+QhdP+fSpV2+4W/+ef6v\nH3gNd16+Mz+CCmiF56wVB4cHnD37rFyUk5VmVVlIPUdHx+zunpHvpcjp6YnoFedMuu87cTjOAl0p\nWq5dK0FYtmSJLOcnWA1GJZ7xjKfRnXp+5W3v4PrRimfs7xMQdxabGU+y3V26rsdETxwCxhmxm0oC\nWQWfxCzWJybVlOXccXR94LDrOV3MH5Y4d1MHYRI5QErgDTGgkhpvaCJYLQ0SKQm+VAj5JfMzOise\nacm4ktYkL3SlxFq20mwELBUTmuLGLBlXComYAk09EV5EppBZW+OcqLapGGmrCiolvGFyoM1L4JSd\nJ5p2SopRbImsOHmAJDRp8OjcWkuEGPyodxtz0KlqjTYVMVsfmdqgKoNPXtikGxCNSLYkUki4OLA9\n2UIrs/Zf05qtusX7wFYzoW0ahtyRqJKSxgG0dAflbkSiWCbNWsGCtdUMbiChsVUtRT8jbd9WS3AO\nPmC1oa4qUcYDbKG6BaFQzWzNdDKl6zq641MqW7Fdt0wnU2kLVopVCFg0VltcTPm8i+B4lRQ+JkJS\n7DRiWy5QkNwf/dBnHY+KvuupszC+956Y8egYIzaqsauvjokK8ZOzSlZFzjlsZaVo2a9YpiWTM1tM\nplNRilutaKZTFouF7AOii6yUou89s1qxuzvh9NRS2ZZPfOIKj7/rGTR14PMefxfL0yMJ9CqhVAKl\nsdUUEMfjC+fP0A9Lhn4lOs1J1PaSMnRuQdUYTk4PhA3gBBaQ9EI66w6PH2A2qeiXJ2xNgJQYuo6m\n2WLVDWCkWDbbmhKSY1IlTk4Omc1aYlpglKV3A0dHgbNnj3nUbRewRvNnnv0k7vvYAQCH8w6va2ZV\nhUmw8gN7+1tUCaK21K3QHqMznB73zLZatA3QW5bzQN1UAkGi6HzCVxqrQUcIbuC076j3Z5gUCE7s\nvo5Pluzs1kymFd6t+MqvfA7a1Pz7t7wLUs1qFYRxpC1DNBwsV9z+mNvEHssY8WiMkWEFQ0IK1J3H\nNAZlDf2qp7E7+BhJuuKxd11+WMLcH3S5u4mGLoUmpej6XnDajAGmlDC2oqoaikg06Kx0VTRdNShD\n0hXoCp80PiAawKgRLxRxEsBUJGPwKKI2kuFpK/9WCmUrgtaEUaQdVkOPT57lsBTNVgIuOnyI9FmL\nQFdW1NgyJWvwjs45VFbGStnOQGlL1zspuqCpmwnGWvoYxcFCK4LVDCRUXUFtGVTC66J1oaXdVRux\nYdEWbWp8UGhdU1UNfRg4Xc1x0aMqg2kqHBGM2CYNUgkiaIuqG2Jd44whVBbdttA0qMmE1DYsYyRa\ni0ug6xYXIjGJQH7VTETcqJkSIhhbYauG3gdSYUf4QDWdYadTkjGopmIVBlJtqGYTBhWx05ZYabwG\nVRlqbdFJWlmndYtVijh4Jk2D7weSF0pe5zpCCvjo6b1cI1MJ/a93PfWkRllDO52K2H/b5op8IIZA\nU9dM82u6siSlGFQi1RY9qVFtzZCDm7aG09NTDg8OODk9oRt6FqulCARNpFClpw3omll9jhgGLt52\nBqMbXG/Z3dpH6zm724k4LKU7Uicq42gqjzUeFTMeniLd6jqDO0TZQF1ZGmUwX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MBaamNY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\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4bhYL4Ua5y4dux+bARq7cW2vFsXlYF/02JSxLcCn7UnivhVJVzluMceS5jt1iGeYAbjgu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a8c2adf50>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_label_name = metadata.features['label'].int2str\n",
    "\n",
    "for image, label in raw_train.take(2):\n",
    "  plt.figure()\n",
    "  plt.imshow(image)\n",
    "  plt.title(get_label_name(label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wvidPx6jeFzf"
   },
   "source": [
    "### Format the Data\n",
    "\n",
    "Use the `tf.image` module to format the images for the task.\n",
    "\n",
    "Resize the images to a fixes input size, and rescale the input channels to a range of `[-1,1]`\n",
    "\n",
    "<!-- TODO(markdaoust): fix the keras_applications preprocessing functions to work in tf2 -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y3PM6GVHcC31"
   },
   "outputs": [],
   "source": [
    "IMG_SIZE = 160 # All images will be resized to 160x160\n",
    "\n",
    "def format_example(image, label):\n",
    "  image = tf.cast(image, tf.float32)\n",
    "  image = (image/127.5) - 1\n",
    "  image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
    "  return image, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i2MRh_AeBtOM"
   },
   "source": [
    "Apply this function to each item in the dataset using the map method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SFZ6ZW7KSXP9"
   },
   "outputs": [],
   "source": [
    "train = raw_train.map(format_example)\n",
    "validation = raw_validation.map(format_example)\n",
    "test = raw_test.map(format_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E5ifgXDuBfOC"
   },
   "source": [
    "Now shuffle and batch the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yic-I66m6Isv"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "SHUFFLE_BUFFER_SIZE = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p3UUPdm86LNC"
   },
   "outputs": [],
   "source": [
    "train_batches = train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
    "validation_batches = validation.batch(BATCH_SIZE)\n",
    "test_batches = test.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "02rJpcFtChP0"
   },
   "source": [
    "Inspect a batch of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "iknFo3ELBVho",
    "outputId": "6daad7cf-5151-44f8-87e8-40ed76cf406f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 160, 160, 3])"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for image_batch, label_batch in train_batches.take(1):\n",
    "  pass\n",
    "\n",
    "image_batch.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OkH-kazQecHB"
   },
   "source": [
    "## Create the base model from the pre-trained convnets\n",
    "You will create the base model from the **MobileNet V2** model developed at Google. This is pre-trained on the ImageNet dataset, a large dataset of 1.4M images and 1000 classes of web images. ImageNet has a fairly arbitrary research training dataset with categories like `jackfruit` and `syringe`, but this base of knowledge will help us tell apart cats and dogs from our specific dataset.\n",
    "\n",
    "First, you need to pick which layer of MobileNet V2 you will use for feature extraction. Obviously, the very last classification layer (on \"top\", as most diagrams of machine learning models go from bottom to top) is not very useful.  Instead, you will follow the common practice to instead depend on the very last layer before the flatten operation. This layer is called the \"bottleneck layer\". The bottleneck features retain much generality as compared to the final/top layer.\n",
    "\n",
    "First, instantiate a MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the **include_top=False** argument, you load a network that doesn't include the classification layers at the top, which is ideal for feature extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "19IQ2gqneqmS",
    "outputId": "2659862d-e726-47b0-cdd9-355938312718"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5\n",
      "9412608/9406464 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)\n",
    "\n",
    "# Create the base model from the pre-trained model MobileNet V2\n",
    "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AqcsxoJIEVXZ"
   },
   "source": [
    "This feature extractor converts each `160x160x3` image to a `5x5x1280` block of features. See what it does to the example batch of images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Y-2LJL0EEUcx",
    "outputId": "408397a1-880f-450f-f9c8-bdc39bf6b83a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 5, 5, 1280)\n"
     ]
    }
   ],
   "source": [
    "feature_batch = base_model(image_batch)\n",
    "print(feature_batch.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rlx56nQtfe8Y"
   },
   "source": [
    "## Feature extraction\n",
    "You will freeze the convolutional base created from the previous step and use that as a feature extractor, add a classifier on top of it and train the top-level classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CnMLieHBCwil"
   },
   "source": [
    "### Freeze the convolutional base\n",
    "It's important to freeze the convolutional based before you compile and train the model. By freezing (or setting `layer.trainable = False`), you prevent the weights in a given layer from being updated during training. MobileNet V2 has many layers, so setting the entire model's trainable flag to `False` will freeze all the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OTCJH4bphOeo"
   },
   "outputs": [],
   "source": [
    "base_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 5593
    },
    "colab_type": "code",
    "id": "KpbzSmPkDa-N",
    "outputId": "a6e319e3-92de-4153-b10a-d7b34d0e4f6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mobilenetv2_1.00_160\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to\n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 160, 160, 3) 0\n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, 161, 161, 3)  0           input_1[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 80, 80, 32)   864         Conv1_pad[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalizationV1) (None, 80, 80, 32)   128         Conv1[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 80, 80, 32)   0           bn_Conv1[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128         expanded_conv_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 80, 80, 32)   0           expanded_conv_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 80, 80, 16)   64          expanded_conv_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 80, 80, 96)   1536        expanded_conv_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 80, 80, 96)   384         block_1_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 80, 80, 96)   0           block_1_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 81, 81, 96)   0           block_1_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864         block_1_pad[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384         block_1_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0           block_1_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 40, 40, 24)   2304        block_1_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 40, 40, 24)   96          block_1_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 40, 40, 144)  3456        block_1_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_2_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_2_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 40, 40, 144)  1296        block_2_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)  576         block_2_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 40, 40, 144)  0           block_2_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 40, 40, 24)   3456        block_2_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 40, 40, 24)   96          block_2_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 40, 40, 24)   0           block_1_project_BN[0][0]\n",
      "                                                                 block_2_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 40, 40, 144)  3456        block_2_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_3_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_3_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 41, 41, 144)  0           block_3_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296        block_3_pad[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)  576         block_3_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)  0           block_3_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 20, 20, 32)   4608        block_3_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 20, 20, 32)   128         block_3_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 20, 20, 192)  6144        block_3_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_4_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_4_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_4_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_4_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_4_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 20, 20, 32)   6144        block_4_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 20, 20, 32)   128         block_4_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 20, 20, 32)   0           block_3_project_BN[0][0]\n",
      "                                                                 block_4_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 20, 20, 192)  6144        block_4_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_5_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_5_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_5_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_5_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_5_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 20, 20, 32)   6144        block_5_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 20, 20, 32)   128         block_5_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 20, 20, 32)   0           block_4_add[0][0]\n",
      "                                                                 block_5_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 20, 20, 192)  6144        block_5_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_6_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_6_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 21, 21, 192)  0           block_6_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 10, 10, 192)  1728        block_6_pad[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)  768         block_6_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 10, 10, 192)  0           block_6_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 10, 10, 64)   12288       block_6_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 10, 10, 64)   256         block_6_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 10, 10, 384)  24576       block_6_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_7_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_7_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_7_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_7_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 10, 10, 64)   24576       block_7_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 10, 10, 64)   256         block_7_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 10, 10, 64)   0           block_6_project_BN[0][0]\n",
      "                                                                 block_7_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 10, 10, 384)  24576       block_7_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_8_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_8_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_8_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_8_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_8_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 10, 10, 64)   24576       block_8_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 10, 10, 64)   256         block_8_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 10, 10, 64)   0           block_7_add[0][0]\n",
      "                                                                 block_8_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 10, 10, 384)  24576       block_8_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_9_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_9_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_9_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_9_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_9_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 10, 10, 64)   24576       block_9_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 10, 10, 64)   256         block_9_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 10, 10, 64)   0           block_8_add[0][0]\n",
      "                                                                 block_9_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 10, 10, 384)  24576       block_9_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 10, 10, 384)  1536        block_10_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 10, 10, 384)  0           block_10_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 10, 10, 384)  3456        block_10_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 10, 10, 384)  1536        block_10_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           block_10_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 10, 10, 96)   36864       block_10_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 10, 10, 96)   384         block_10_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 10, 10, 576)  55296       block_10_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_11_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_11_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_11_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_11_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_11_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 10, 10, 96)   55296       block_11_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 10, 10, 96)   384         block_11_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 10, 10, 96)   0           block_10_project_BN[0][0]\n",
      "                                                                 block_11_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 10, 10, 576)  55296       block_11_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_12_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_12_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_12_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_12_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_12_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 10, 10, 96)   55296       block_12_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 10, 10, 96)   384         block_12_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 10, 10, 96)   0           block_11_add[0][0]\n",
      "                                                                 block_12_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 10, 10, 576)  55296       block_12_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_13_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_13_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 11, 11, 576)  0           block_13_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 5, 5, 576)    5184        block_13_pad[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 5, 5, 576)    2304        block_13_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)    0           block_13_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 5, 5, 160)    92160       block_13_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 5, 5, 160)    640         block_13_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 5, 5, 960)    153600      block_13_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_14_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_14_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_14_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_14_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_14_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 5, 5, 160)    153600      block_14_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 5, 5, 160)    640         block_14_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 5, 5, 160)    0           block_13_project_BN[0][0]\n",
      "                                                                 block_14_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 5, 5, 960)    153600      block_14_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_15_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_15_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_15_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_15_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_15_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 5, 5, 160)    153600      block_15_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 5, 5, 160)    640         block_15_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 5, 5, 160)    0           block_14_add[0][0]\n",
      "                                                                 block_15_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 5, 5, 960)    153600      block_15_add[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_16_expand[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_16_expand_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_16_expand_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_16_depthwise[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_16_depthwise_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 5, 5, 320)    307200      block_16_depthwise_relu[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 5, 5, 320)    1280        block_16_project[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 5, 5, 1280)   409600      block_16_project_BN[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalizationV1 (None, 5, 5, 1280)   5120        Conv_1[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 5, 5, 1280)   0           Conv_1_bn[0][0]\n",
      "==================================================================================================\n",
      "Total params: 2,257,984\n",
      "Trainable params: 0\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Let's take a look at the base model architecture\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wdMRM8YModbk"
   },
   "source": [
    "### Add a classification head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QBc31c4tMOdH"
   },
   "source": [
    "To generate predictions from the block of features, average over the spatial `5x5` spatial locations, using a `tf.keras.layers.GlobalAveragePooling2D` layer to convert the features to  a single 1280-element vector per image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dLnpMF5KOALm",
    "outputId": "652a7b64-d9a4-4bf8-936c-f75d74afaa35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 1280)\n"
     ]
    }
   ],
   "source": [
    "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "feature_batch_average = global_average_layer(feature_batch)\n",
    "print(feature_batch_average.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O1p0OJBR6dOT"
   },
   "source": [
    "Apply a `tf.keras.layers.Dense` layer to convert these features into a single prediction per image. You don't need an activation function here because this prediction will be treated as a `logit`, or a raw prediciton value.  Positive numbers predict class 1, negative numbers predict class 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Wv4afXKj6cVa",
    "outputId": "aa2cd81f-016e-4650-deba-10b48c36e727"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 1)\n"
     ]
    }
   ],
   "source": [
    "prediction_layer = keras.layers.Dense(1)\n",
    "prediction_batch = prediction_layer(feature_batch_average)\n",
    "print(prediction_batch.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0iqnBeZrfoIc"
   },
   "source": [
    "Now stack the feature extractor, and these two layers using a `tf.keras.Sequential` model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eApvroIyn1K0"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "  base_model,\n",
    "  global_average_layer,\n",
    "  prediction_layer\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g0ylJXE_kRLi"
   },
   "source": [
    "### Compile the model\n",
    "\n",
    "You must compile the model before training it.  Since there are two classes, use a binary cross-entropy loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RpR8HdyMhukJ"
   },
   "outputs": [],
   "source": [
    "base_learning_rate = 0.0001\n",
    "model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "I8ARiyMFsgbH",
    "outputId": "b526ba94-551e-47c8-bf37-22c35b0d1c5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #\n",
      "=================================================================\n",
      "mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280)        2257984\n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1280)              0\n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 1281\n",
      "=================================================================\n",
      "Total params: 2,259,265\n",
      "Trainable params: 1,281\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lxOcmVr0ydFZ"
   },
   "source": [
    "The 2.5M parameters in MobileNet are frozen, but there are 1.2K _trainable_ parameters in the Dense layer.  These are divided between two `tf.Variable` objects, the weights and biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "krvBumovycVA",
    "outputId": "cb122b3e-f832-4bd6-c47d-19c18e92ed9b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RxvgOYTDSWTx"
   },
   "source": [
    "### Train the model\n",
    "\n",
    "After training for 10 epochs, you should see ~96% accuracy.\n",
    "\n",
    "<!-- TODO(markdaoust): delete steps_per_epoch in TensorFlow r1.14/r2.0 -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hlHEavK7DUI7"
   },
   "outputs": [],
   "source": [
    "num_train, num_val, num_test = (\n",
    "  metadata.splits['train'].num_examples*weight/10\n",
    "  for weight in SPLIT_WEIGHTS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Om4O3EESkab1",
    "outputId": "77b1af8a-4caf-4fcc-8d92-609ddabc6f29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 4s 219ms/step - loss: 3.1885 - accuracy: 0.6109\n"
     ]
    }
   ],
   "source": [
    "initial_epochs = 10\n",
    "steps_per_epoch = round(num_train)//BATCH_SIZE\n",
    "validation_steps = 20\n",
    "\n",
    "loss0,accuracy0 = model.evaluate(validation_batches, steps = validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "8cYT1c48CuSd",
    "outputId": "8026556e-51c9-4173-c5de-e4e0df821e31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial loss: 3.19\n",
      "initial accuracy: 0.61\n"
     ]
    }
   ],
   "source": [
    "print(\"initial loss: {:.2f}\".format(loss0))\n",
    "print(\"initial accuracy: {:.2f}\".format(accuracy0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "JsaRFlZ9B6WK",
    "outputId": "9d5c9af0-c756-410f-e203-68c869be6801"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "581/581 [==============================] - 102s 175ms/step - loss: 1.8917 - accuracy: 0.7606 - val_loss: 0.8860 - val_accuracy: 0.8828\n",
      "Epoch 2/10\n",
      "581/581 [==============================] - 94s 161ms/step - loss: 0.9989 - accuracy: 0.8697 - val_loss: 0.4330 - val_accuracy: 0.9281\n",
      "Epoch 3/10\n",
      "581/581 [==============================] - 99s 170ms/step - loss: 0.7417 - accuracy: 0.8995 - val_loss: 0.2629 - val_accuracy: 0.9469\n",
      "Epoch 4/10\n",
      "581/581 [==============================] - 94s 162ms/step - loss: 0.6681 - accuracy: 0.9133 - val_loss: 0.2753 - val_accuracy: 0.9563\n",
      "Epoch 5/10\n",
      "581/581 [==============================] - 98s 169ms/step - loss: 0.5944 - accuracy: 0.9220 - val_loss: 0.2410 - val_accuracy: 0.9641\n",
      "Epoch 6/10\n",
      "581/581 [==============================] - 98s 169ms/step - loss: 0.5779 - accuracy: 0.9252 - val_loss: 0.2003 - val_accuracy: 0.9656\n",
      "Epoch 7/10\n",
      "581/581 [==============================] - 94s 162ms/step - loss: 0.5151 - accuracy: 0.9328 - val_loss: 0.2115 - val_accuracy: 0.9688\n",
      "Epoch 8/10\n",
      "581/581 [==============================] - 95s 164ms/step - loss: 0.5076 - accuracy: 0.9346 - val_loss: 0.1678 - val_accuracy: 0.9703\n",
      "Epoch 9/10\n",
      "581/581 [==============================] - 96s 165ms/step - loss: 0.4679 - accuracy: 0.9368 - val_loss: 0.1668 - val_accuracy: 0.9703\n",
      "Epoch 10/10\n",
      "581/581 [==============================] - 96s 165ms/step - loss: 0.4921 - accuracy: 0.9381 - val_loss: 0.1847 - val_accuracy: 0.9719\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_batches,\n",
    "                    epochs=initial_epochs,\n",
    "                    validation_data=validation_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hd94CKImf8vi"
   },
   "source": [
    "### Learning curves\n",
    "\n",
    "Let's take a look at the learning curves of the training and validation accuracy/loss when using the MobileNet V2 base model as a fixed feature extractor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 512
    },
    "colab_type": "code",
    "id": "53OTCh3jnbwV",
    "outputId": "3e7ed689-c5ad-4cfd-8416-1a02e34ac953"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SQtQ/EuoOYFWsfHduK9+e3YxOrWNqp0n0Du7p6LKajLxCE6dLA/xkUhYXruSi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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([min(plt.ylim()),1])\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('Cross Entropy')\n",
    "plt.ylim([0,1.0])\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "foWMyyUHbc1j"
   },
   "source": [
    "Note: If you are wondering why the validation metrics are clearly better than the training metrics, the main factor is because layers like `tf.keras.layers.BatchNormalization` and `tf.keras.layers.Dropout` affect accuracy during training. They are turned off when calculating validation loss.\n",
    "\n",
    "To a lesser extent, it is also because training metrics report the average for an epoch, while validation metrics are evaluated after the epoch, so validation metrics see a model that has trained slightly longer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CqwV-CRdS6Nv"
   },
   "source": [
    "## Fine tuning\n",
    "In our feature extraction experiment, you were only training a few layers on top of an MobileNet V2 base model. The weights of the pre-trained network were **not** updated during training.\n",
    "\n",
    "One way to increase performance even further is to train (or \"fine-tune\") the weights of the top layers of the pre-trained model alongside the training of the classifier you added. The training process will force the weights to be tuned from generic features maps to features associated specifically to our dataset.\n",
    "\n",
    "Note: This should only be attempted after you have trained the top-level classifier with the pre-trained model set to non-trainable. If you add a randomly initialized classifier on top of a pre-trained model and attempt to train all layers jointly, the magnitude of the gradient updates will be too large (due to the random weights from the classifier) and your pre-trained model will forget what it has learned.\n",
    "\n",
    "Also, you should try to fine-tune a small number of top layers rather than the whole MobileNet model. In most convolutional networks, the higher up a layer is, the more specialized it is. The first few layers learn very simple and generic features which generalize to almost all types of images. As you go higher up, the features are increasingly more specific to the dataset on which the model was trained. The goal of fine-tuning is to adapt these specialized features to work with the new dataset, rather than overwrite the generic learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CPXnzUK0QonF"
   },
   "source": [
    "### Un-freeze the top layers of the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rfxv_ifotQak"
   },
   "source": [
    "All you need to do is unfreeze the `base_model` and set the bottom layers be un-trainable. Then, you should recompile the model (necessary for these changes to take effect), and resume training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4nzcagVitLQm"
   },
   "outputs": [],
   "source": [
    "base_model.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-4HgVAacRs5v",
    "outputId": "9ce5967c-8d22-45bd-84ff-c34ef57c1ec1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of layers in the base model:  155\n"
     ]
    }
   ],
   "source": [
    "# Let's take a look to see how many layers are in the base model\n",
    "print(\"Number of layers in the base model: \", len(base_model.layers))\n",
    "\n",
    "# Fine tune from this layer onwards\n",
    "fine_tune_at = 100\n",
    "\n",
    "# Freeze all the layers before the `fine_tune_at` layer\n",
    "for layer in base_model.layers[:fine_tune_at]:\n",
    "  layer.trainable =  False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4Uk1dgsxT0IS"
   },
   "source": [
    "### Compile the model\n",
    "\n",
    "Compile the model using a much lower training rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NtUnaz0WUDva"
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer = tf.keras.optimizers.RMSprop(lr=base_learning_rate/10),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "WwBWy7J2kZvA",
    "outputId": "d7bab642-9e06-4790-aacb-20e6924079a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #\n",
      "=================================================================\n",
      "mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280)        2257984\n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1280)              0\n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 1281\n",
      "=================================================================\n",
      "Total params: 2,259,265\n",
      "Trainable params: 1,863,873\n",
      "Non-trainable params: 395,392\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bNXelbMQtonr",
    "outputId": "1d7df962-139b-4e25-8367-4533680b79ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4G5O4jd6TuAG"
   },
   "source": [
    "### Continue Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0foWUN-yDLo_"
   },
   "source": [
    "If you trained to convergence earlier, this will get you a few percent more accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "ECQLkAsFTlun",
    "outputId": "4532df99-0c58-4321-baeb-7b6a2432c4fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/20\n",
      "581/581 [==============================] - 134s 231ms/step - loss: 0.4208 - accuracy: 0.9484 - val_loss: 0.1907 - val_accuracy: 0.9812\n",
      "Epoch 12/20\n",
      "581/581 [==============================] - 114s 197ms/step - loss: 0.3359 - accuracy: 0.9570 - val_loss: 0.1835 - val_accuracy: 0.9844\n",
      "Epoch 13/20\n",
      "581/581 [==============================] - 116s 200ms/step - loss: 0.2930 - accuracy: 0.9650 - val_loss: 0.1505 - val_accuracy: 0.9844\n",
      "Epoch 14/20\n",
      "581/581 [==============================] - 114s 196ms/step - loss: 0.2561 - accuracy: 0.9701 - val_loss: 0.1575 - val_accuracy: 0.9859\n",
      "Epoch 15/20\n",
      "581/581 [==============================] - 119s 206ms/step - loss: 0.2302 - accuracy: 0.9715 - val_loss: 0.1600 - val_accuracy: 0.9812\n",
      "Epoch 16/20\n",
      "581/581 [==============================] - 115s 197ms/step - loss: 0.2134 - accuracy: 0.9747 - val_loss: 0.1407 - val_accuracy: 0.9828\n",
      "Epoch 17/20\n",
      "581/581 [==============================] - 115s 197ms/step - loss: 0.1546 - accuracy: 0.9813 - val_loss: 0.0944 - val_accuracy: 0.9828\n",
      "Epoch 18/20\n",
      "581/581 [==============================] - 116s 200ms/step - loss: 0.1636 - accuracy: 0.9794 - val_loss: 0.0947 - val_accuracy: 0.9844\n",
      "Epoch 19/20\n",
      "581/581 [==============================] - 115s 198ms/step - loss: 0.1356 - accuracy: 0.9823 - val_loss: 0.1169 - val_accuracy: 0.9828\n",
      "Epoch 20/20\n",
      "581/581 [==============================] - 116s 199ms/step - loss: 0.1243 - accuracy: 0.9849 - val_loss: 0.1121 - val_accuracy: 0.9875\n"
     ]
    }
   ],
   "source": [
    "fine_tune_epochs = 10\n",
    "total_epochs =  initial_epochs + fine_tune_epochs\n",
    "\n",
    "history_fine = model.fit(train_batches,\n",
    "                         epochs=total_epochs,\n",
    "                         initial_epoch = initial_epochs,\n",
    "                         validation_data=validation_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TfXEmsxQf6eP"
   },
   "source": [
    "Let's take a look at the learning curves of the training and validation accuracy/loss, when fine tuning the last few layers of the MobileNet V2 base model and training the classifier on top of it. The validation loss is much higher than the training loss, so you may get some overfitting.\n",
    "\n",
    "You may also get some overfitting as the new training set is relatively small and similar to the original MobileNet V2 datasets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DNtfNZKlInGT"
   },
   "source": [
    "After fine tuning the model nearly reaches 98% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PpA8PlpQKygw"
   },
   "outputs": [],
   "source": [
    "acc += history_fine.history['accuracy']\n",
    "val_acc += history_fine.history['val_accuracy']\n",
    "\n",
    "loss += history_fine.history['loss']\n",
    "val_loss += history_fine.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 512
    },
    "colab_type": "code",
    "id": "chW103JUItdk",
    "outputId": "c681fe0e-a256-48e1-e9e1-8e6e4667e945"
   },
   "outputs": [
    {
     "data": {
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dwiFbKUV1JRysL6Go/hA1TVbf6ypUxIWZ6R+eQr/wZPqF9yXJ2KfbyidJWwhxQbC32Kl0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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.ylim([0.8, 1])\n",
    "plt.plot([initial_epochs-1,initial_epochs-1],\n",
    "          plt.ylim(), label='Start Fine Tuning')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.ylim([0, 1.0])\n",
    "plt.plot([initial_epochs-1,initial_epochs-1],\n",
    "         plt.ylim(), label='Start Fine Tuning')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_TZTwG7nhm0C"
   },
   "source": [
    "## Summary:\n",
    "\n",
    "* **Using a pre-trained model for feature extraction**:  When working with a small dataset, it is common to take advantage of features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully-connected classifier on top. The pre-trained model is \"frozen\" and only the weights of the classifier get updated during training.\n",
    "In this case, the convolutional base extracted all the features associated with each image and you just trained a classifier that determines the image class given that set of extracted features.\n",
    "\n",
    "* **Fine-tuning a pre-trained model**: To further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning.\n",
    "In this case, you tuned your weights such that your model learned high-level features specific to the dataset. This technique is usually recommended when the training dataset is large and very similar to the orginial dataset that the pre-trained model was trained on.\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "transfer_learning.ipynb",
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
